Can humans solve social navigation problems?
In the paper, we start by examining social navigation behaviors in a
one-day session (Study 1), or in the first session of a two-day study
(Studies 2-3). Across all three studies, the procedure is exactly
identical; studies 2-3 are, in this part of the dataset, exact
replications of study 1.
nav_study1 <- here("data", "clean-data", "study1_message_passing.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == FALSE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(
study = "Study 1",
measurement_id = str_c("D", measurement_id),
shortest_path = factor(shortest_path_given_opts)
) %>%
select(
study, sub_id, measurement_id, shortest_path,
startpoint_id, endpoint_id,
opt1_id, opt2_id,
correct_choice, sub_choice,
correct, rt
)
nav_study2 <- here("data", "clean-data", "study2_message_passing.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == FALSE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(
study = "Study 2",
measurement_id = case_when(
network == "learned" ~ str_c("D", measurement_id),
network == "reevaluated" ~ "D2b"
),
shortest_path = factor(shortest_path_given_opts)
) %>%
select(
study, sub_id, measurement_id, shortest_path,
startpoint_id, endpoint_id,
opt1_id, opt2_id,
correct_choice, sub_choice,
correct, rt
)
nav_study3 <- here("data", "clean-data", "study3_message_passing.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == FALSE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(
study = "Study 3",
measurement_id = case_when(
network == "reevaluated" ~ "D2b",
measurement_id == 1 ~ "D1",
measurement_id == 2 ~ "D1b",
measurement_id == 3 ~ "D2"
),
shortest_path = factor(shortest_path_given_opts)
) %>%
select(
study, sub_id, measurement_id, shortest_path,
startpoint_id, endpoint_id,
opt1_id, opt2_id,
correct_choice, sub_choice,
correct, rt
)
We’ll start off with some descriptive statistics of human behavior.
To maximize statistical power, we will pool across studies whenever
possible.
bind_rows(nav_study1, nav_study2, nav_study3) %>%
filter(measurement_id == "D1") %>%
group_by(measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
arrange(measurement_id, shortest_path) %>%
pivot_wider(
names_from = shortest_path, values_from = accuracy, names_prefix = "dist-"
) %>%
kbl(
caption = str_c(
"<center>", "Descriptive: Navigation accuracy", "</center>"
),
digits = 2
) %>%
kable_styling(bootstrap_options = c("responsive"))
Descriptive: Navigation accuracy
|
measurement_id
|
dist-2
|
dist-3
|
dist-4
|
|
D1
|
0.8
|
0.7
|
0.63
|
And now the inferential statistical tests. Note that we’re interested
in knowing whether navigation accuracy differs from chance at each
distance, so we’ll estimate the same statistical model multiple times,
changing the reference category each time. Note that this only
reparameterizes the model, such that the same variance is
accounted for by different parameters; it does not change the
total amount of variance accounted for.
nav_day1 <- bind_rows(nav_study1, nav_study2, nav_study3) %>%
filter(measurement_id == "D1") %>%
# Give every subject a distinct identifier
mutate(sub_id = str_c(study, " s", sub_id))
stats_nav_day1_dist2 <- nav_day1 %>%
mutate(shortest_path = fct_relevel(shortest_path, "2")) %>%
glmmTMB(
correct ~ shortest_path + (1 + shortest_path | sub_id) + (1 | study),
family = binomial,
data = .
)
stats_nav_day1_dist3 <- nav_day1 %>%
mutate(shortest_path = fct_relevel(shortest_path, "3")) %>%
glmmTMB(
correct ~ shortest_path + (1 + shortest_path | sub_id) + (1 | study),
family = binomial,
data = .
)
stats_nav_day1_dist4 <- nav_day1 %>%
mutate(shortest_path = fct_relevel(shortest_path, "4")) %>%
glmmTMB(
correct ~ shortest_path + (1 + shortest_path | sub_id) + (1 | study),
family = binomial,
data = .
)
map_dfr(
.x = list(
"dist-2" = stats_nav_day1_dist2,
"dist-3" = stats_nav_day1_dist3,
"dist-4" = stats_nav_day1_dist4
),
.f = ~tidy(.x, conf.int = TRUE),
.id = "ref_cat"
) %>%
check_significance() %>%
select(-c(ref_cat, effect, component)) %>%
kbl(
caption = str_c("<center>", "Navigation accuracy: Day 1", "</center>"),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive")) %>%
pack_rows("Ref. Cat. dist-2", 1, 10) %>%
pack_rows("Ref. Cat. dist-3", 11, 20) %>%
pack_rows("Ref. Cat. dist-4", 21, 30)
Navigation accuracy: Day 1
|
group
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
conf.low
|
conf.high
|
sig
|
|
Ref. Cat. dist-2
|
|
NA
|
(Intercept)
|
1.682
|
0.118
|
14.303
|
0
|
1.451
|
1.912
|
***
|
|
NA
|
shortest_path3
|
-0.622
|
0.066
|
-9.428
|
0
|
-0.751
|
-0.493
|
***
|
|
NA
|
shortest_path4
|
-1.026
|
0.081
|
-12.710
|
0
|
-1.184
|
-0.868
|
***
|
|
sub_id
|
sd__(Intercept)
|
1.041
|
NA
|
NA
|
NA
|
0.897
|
1.208
|
|
|
sub_id
|
sd__shortest_path3
|
0.437
|
NA
|
NA
|
NA
|
0.320
|
0.596
|
|
|
sub_id
|
sd__shortest_path4
|
0.748
|
NA
|
NA
|
NA
|
0.624
|
0.897
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
-0.392
|
NA
|
NA
|
NA
|
-0.645
|
-0.008
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.538
|
NA
|
NA
|
NA
|
-0.884
|
0.179
|
|
|
sub_id
|
cor__shortest_path3.shortest_path4
|
0.944
|
NA
|
NA
|
NA
|
0.236
|
0.976
|
|
|
study
|
sd__(Intercept)
|
0.122
|
NA
|
NA
|
NA
|
0.019
|
0.765
|
|
|
Ref. Cat. dist-3
|
|
NA
|
(Intercept)
|
1.059
|
0.114
|
9.335
|
0
|
0.837
|
1.282
|
***
|
|
NA
|
shortest_path2
|
0.622
|
0.066
|
9.428
|
0
|
0.493
|
0.751
|
***
|
|
NA
|
shortest_path4
|
-0.404
|
0.062
|
-6.549
|
0
|
-0.525
|
-0.283
|
***
|
|
sub_id
|
sd__(Intercept)
|
0.958
|
NA
|
NA
|
NA
|
0.807
|
1.137
|
|
|
sub_id
|
sd__shortest_path2
|
0.437
|
NA
|
NA
|
NA
|
0.320
|
0.596
|
|
|
sub_id
|
sd__shortest_path4
|
0.366
|
NA
|
NA
|
NA
|
0.243
|
0.549
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.030
|
NA
|
NA
|
NA
|
-0.398
|
0.351
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.352
|
NA
|
NA
|
NA
|
-0.364
|
0.248
|
|
|
sub_id
|
cor__shortest_path2.shortest_path4
|
-0.737
|
NA
|
NA
|
NA
|
-0.688
|
0.874
|
|
|
study
|
sd__(Intercept)
|
0.122
|
NA
|
NA
|
NA
|
0.019
|
0.765
|
|
|
Ref. Cat. dist-4
|
|
NA
|
(Intercept)
|
0.655
|
0.108
|
6.049
|
0
|
0.443
|
0.868
|
***
|
|
NA
|
shortest_path2
|
1.026
|
0.081
|
12.709
|
0
|
0.868
|
1.184
|
***
|
|
NA
|
shortest_path3
|
0.404
|
0.062
|
6.549
|
0
|
0.283
|
0.525
|
***
|
|
sub_id
|
sd__(Intercept)
|
0.897
|
NA
|
NA
|
NA
|
0.765
|
1.053
|
|
|
sub_id
|
sd__shortest_path2
|
0.748
|
NA
|
NA
|
NA
|
0.624
|
0.897
|
|
|
sub_id
|
sd__shortest_path3
|
0.366
|
NA
|
NA
|
NA
|
0.243
|
0.549
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.209
|
NA
|
NA
|
NA
|
-0.432
|
0.051
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
-0.031
|
NA
|
NA
|
NA
|
-0.472
|
0.151
|
|
|
sub_id
|
cor__shortest_path2.shortest_path3
|
0.919
|
NA
|
NA
|
NA
|
-0.524
|
0.986
|
|
|
study
|
sd__(Intercept)
|
0.122
|
NA
|
NA
|
NA
|
0.019
|
0.765
|
|
We’ll plot out the raw data, plus model predictions…
predict_nav_day1 <- expand_grid(
measurement_id = "D1",
shortest_path = factor(2:4),
sub_id = NA, study = NA
) %>%
predict_glmmTMB(stats_nav_day1_dist2)
plot_nav_day1 <- bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id == "D1") %>%
group_by(sub_id, measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
ggplot(aes(x=shortest_path, y=accuracy)) +
theme_custom() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_dotplot(
binwidth = 0.01,
binaxis = "y", stackdir = "center",
position = position_dodge(width = 0.75),
dotsize = 1, alpha = 0.5, color = NA,
show.legend = FALSE
) +
geom_pointrange(
aes(x = shortest_path, y = fit, ymin = fit - se.fit, ymax = fit + se.fit),
data = predict_nav_day1, inherit.aes = FALSE, show.legend = FALSE,
position = position_dodge(width = 0.15), linewidth = 1
) +
geom_line(
aes(x = shortest_path, y = fit, group = measurement_id),
data = predict_nav_day1, inherit.aes = FALSE,
position = position_dodge(width = 0.15), linewidth = 1
) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.1)
) +
coord_cartesian(ylim = c(0.4, 1.1)) +
theme(legend.position = "bottom") +
ggtitle("Human social navigation")
plot_nav_day1

if (knitting) {
ggsave(
here("figures", str_c("navigation_day1", ".pdf")),
plot = plot_nav_day1,
width = 8/3, height = 3,
units = "in", dpi = 300
)
}
Computational models of social navigation
We’d like to have some mechanistic insights about how people solve
navigation problems. To do this, we’ll look at two candidate models of
navigation: breadth-first search (BFS) and the Successor Representation
(SR).
Simulations
BFS simulation
sim_bfs <- here("data", "bfs-sims", "bfs_sims_learned.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == FALSE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(shortest_path = factor(shortest_path_given_opts)) %>%
select(
shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id,
bfs_choice, bfs_correct_choice, bfs_n_visits_total
)
In our implementation, we model the BFS agent having some “threshold”
for searching through the network. This can be thought of as a
“willingness to spend time/effort performing a search” threshold. Once
that threshold is exceeded, it becomes increasingly likely that the
agent gives up and chooses randomly.
To see what threshold values might be informative to look at, we’ll
look at the average number of “searches” that an agent must perform to
make a (non-random) decision.
sim_bfs %>%
group_by(shortest_path) %>%
summarise(avg_n_searches = mean(bfs_n_visits_total)) %>%
kbl(
caption = str_c("<center>", "Average searches in online-BFS", "</center>"),
digits = 2
) %>%
kable_styling(bootstrap_options = c("responsive"))
Average searches in online-BFS
|
shortest_path
|
avg_n_searches
|
|
2
|
4.92
|
|
3
|
7.70
|
|
4
|
11.16
|
And now we’ll plot the model predictions…
bfs_avg_accuracy <- sim_bfs %>%
group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
summarise(
bfs_accuracy = mean(bfs_correct_choice),
bfs_visits = mean(bfs_n_visits_total),
.groups = "drop"
)
plot_sim_bfs <- bfs_avg_accuracy %>%
expand_grid(search_threshold = seq(2, 12, 2)) %>%
rowwise() %>%
mutate(
# Note: p(BFS) is 1-p(give up)
p_bfs = softmax(
option_values = c(search_threshold, bfs_visits),
option_chosen = 1,
temperature = 1
)
) %>%
ungroup() %>%
# Weigh the model predictions according to their likelihood
mutate(
p_give_up = 1 - p_bfs,
bfs_threshold_accuracy = (p_bfs * bfs_accuracy) + (p_give_up * (1/2))
) %>%
# Format for plotting
mutate(
search_threshold = str_pad(search_threshold, width = 2, side = "left")
) %>%
bind_rows(
bfs_avg_accuracy %>%
mutate(
search_threshold = "Never gives up",
bfs_threshold_accuracy = bfs_accuracy
)
) %>%
# Now plot
ggplot(
aes(
x=shortest_path, y=bfs_threshold_accuracy,
color=search_threshold, group=search_threshold
)
) +
theme_custom() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
stat_summary(geom = "line", fun = mean, linewidth = 1) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(name = "Accuracy", labels = scales::percent) +
scale_color_viridis_d(
name = "Search threshold", option = "magma",
begin = 0.1, end = 0.9, direction = -1
) +
guides(color = guide_legend(byrow = TRUE, nrow = 1)) +
coord_cartesian(ylim = c(0.5, 1)) +
theme(
panel.grid = element_blank(),
legend.position = "bottom"
) +
ggtitle("Simulated BFS navigation")
plot_sim_bfs

if (knitting) {
ggsave(
here("figures", str_c("simulated_bfs", ".pdf")),
plot = plot_sim_bfs,
width = 8/3, height = 3,
units = "in", dpi = 300
)
}
SR navigation simulation
triallist_nav_learned <- nav_study1 %>%
filter(sub_id == 1) %>%
select(
startpoint_id, endpoint_id, opt1_id, opt2_id,
correct_choice, shortest_path
) %>%
arrange(startpoint_id, endpoint_id)
We’ll create some simulated learning observations.
set.seed(sum(utf8ToInt("Jenny and me was like peas and carrots")))
simulated_paired_associates <- adjlist %>%
filter(edge == 1) %>%
select(from, to) %>%
expand_grid(set = 1:5000, .) %>%
group_by(set) %>%
slice_sample(prop = 1) %>%
ungroup()
And now we’ll simulate an “asymptotic” SR and how it performs in the
navigation task.
simulate_sr <- function(simulated_observations) {
simulated_observations %>%
expand_grid(gamma = seq(0.1, 0.9, 0.1)) %>%
group_by(gamma) %>%
nest() %>%
mutate(
sr = map(
.x = data,
.f = ~build_rep_sr(
learning_data = .x, this_alpha = 0.1, this_gamma = gamma
)
)
) %>%
unnest(sr) %>%
ungroup() %>%
select(-data)
}
join_sr <- function(navigation_triallist, simulated_sr) {
navigation_triallist %>%
left_join(
simulated_sr %>%
rename(opt1_sr = sr_value, opt1_id = from, endpoint_id = to)
) %>%
left_join(
simulated_sr %>%
rename(opt2_sr = sr_value, opt2_id = from, endpoint_id = to)
)
}
sim_sr_matrix_asymptotic <- simulated_paired_associates %>%
simulate_sr()
sim_sr_behavior_asymptotic <- join_sr(
triallist_nav_learned, sim_sr_matrix_asymptotic
) %>%
# Feed values through softmax
mutate(across(c(opt1_sr, opt2_sr), ~.x * 100)) %>%
expand_grid(temperature = 1) %>%
rowwise() %>%
mutate(
p_correct = softmax(
option_values = c(opt1_sr, opt2_sr),
option_chosen = if_else(correct_choice == opt1_id, 1, 2),
temperature = temperature,
use_inverse_temperature = TRUE
)
) %>%
ungroup()
## Joining with `by = join_by(endpoint_id, opt1_id)`
## Warning in left_join(., simulated_sr %>% rename(opt1_sr = sr_value, opt1_id = from, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 45 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this
## warning.
## Joining with `by = join_by(endpoint_id, opt2_id, gamma)`
plot_sim_sr_asymptotic <- sim_sr_behavior_asymptotic %>%
mutate(
gamma = factor(gamma),
temperature = str_pad(temperature, width = 2, side = "left"),
temperature = str_c(unicode_greek["tau"], " = ", temperature)
) %>%
group_by(gamma, temperature, shortest_path) %>%
summarise(p_correct = mean(p_correct), .groups = "drop") %>%
ggplot(aes(x=shortest_path, y=p_correct, color=gamma)) +
theme_custom() +
facet_grid(rows = vars(temperature)) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_line(aes(color = gamma, group = gamma), linewidth = 0.8) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(name = "Accuracy", labels = scales::percent) +
scale_color_viridis_d(
name = str_c(unicode_greek["gamma"], " = "), option = "turbo", end = 0.9
) +
guides(color = guide_legend(byrow = TRUE, nrow = 1)) +
coord_cartesian(ylim = c(0.5, 1)) +
theme(legend.position = "bottom") +
ggtitle("Simulated SR navigation")
plot_sim_sr_asymptotic

if (knitting) {
suppressWarnings(
ggsave(
here("figures", str_c("simulated_sr", ".pdf")),
plot = plot_sim_sr_asymptotic,
width = 8/3, height = 3,
units = "in", dpi = 300,
)
)
ggsave(
here("figures", str_c("simulated_sr_cairo", ".pdf")),
plot = plot_sim_sr_asymptotic +
guides(color = guide_legend(byrow = TRUE, nrow = 2)),
width = 8/3, height = 3,
units = "in", dpi = 300,
device = cairo_pdf
)
}
Model comparison
To do model comparison, we’ll need to pull in the estimated
parameters/likelihoods from the model-fitting.
clean_params_from_raw <- FALSE
if (clean_params_from_raw) {
params <- here("data", "param-fits") %>%
fs::dir_ls(regexp = "study[[:digit:]]_D[[:digit:]]b?") %>%
map_dfr(
.f = ~read_csv(.x, show_col_types = FALSE) %>%
best_optim_run("dataframe"),
.id = "filename"
) %>%
mutate(
study = str_extract(filename, "study[[:digit:]]"),
study = str_remove(study, "study"),
study = str_c("Study ", study),
measurement_id = str_extract(filename, "_D[[:digit:]]b?"),
measurement_id = str_remove(measurement_id, "_"),
sub_id = str_extract(filename, "sub-[[:digit:]]+"),
sub_id = str_remove(sub_id, "sub-"),
sub_id = as.integer(sub_id),
model = case_when(
str_detect(filename, "_hybrid-") ~ "hybrid",
str_detect(filename, "_sr-") ~ "sr",
str_detect(filename, "_bfs-") ~ "bfs"
)
) %>%
select(
study, sub_id, measurement_id,
model,
param_name, param_value = param_value_human_readable,
neg_loglik = optim_value
) %>%
arrange(study, sub_id, measurement_id, model)
params %>%
write_csv(here("data", "param-fits", "clean_param_fits.csv"))
} else {
params <- here("data", "param-fits", "clean_param_fits.csv") %>%
read_csv(show_col_types = FALSE)
}
source(here("code", "utils", "bayesian_model_selection.R"))
pxp_results <- params %>%
select(study, sub_id, measurement_id, model, neg_loglik) %>%
distinct() %>%
mutate(log_lik = -neg_loglik) %>%
select(-neg_loglik) %>%
pivot_wider(names_from = model, values_from = log_lik) %>%
select(-sub_id) %>%
group_by(study, measurement_id) %>%
nest() %>%
mutate(
test = map(
.x = data,
.f = ~bayesian_model_selection(.x)
)
) %>%
unnest(test) %>%
ungroup() %>%
select(-data)
pxp_results %>%
ggplot(aes(x=measurement_id, y=pxp, color=model)) +
theme_custom() +
facet_wrap(~study, scales = "free_x") +
geom_point(
position = position_dodge(width = 0.75)
)

pxp_results %>%
select(study, measurement_id, model, pxp) %>%
mutate(pxp = as.numeric(pxp)) %>%
pivot_wider(
names_from = model,
values_from = pxp,
names_prefix = "pxp_"
) %>%
kbl(
caption = str_c("<center>", "PXP results", "</center>"),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive"))
PXP results
|
study
|
measurement_id
|
pxp_bfs
|
pxp_hybrid
|
pxp_sr
|
|
Study 1
|
D1
|
0
|
0.000
|
1.000
|
|
Study 2
|
D1
|
0
|
0.000
|
1.000
|
|
Study 2
|
D2
|
0
|
0.000
|
1.000
|
|
Study 3
|
D1
|
0
|
0.000
|
0.999
|
|
Study 3
|
D1b
|
0
|
0.000
|
1.000
|
|
Study 3
|
D2
|
0
|
0.006
|
0.994
|
Posterior predictive check
We’ll start by simulating the model’s predictions for each subject,
given their estimated parameters.
ppc_bfs <- bind_rows(nav_study1, nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2")) %>%
left_join(
params %>%
filter(model == "bfs") %>%
pivot_wider(names_from = param_name, values_from = param_value) %>%
select(study, sub_id, measurement_id, search_threshold, lapse_rate)
) %>%
left_join(bfs_avg_accuracy) %>%
mutate(
p_sub_choice_bfs = if_else(
sub_choice == correct_choice,
bfs_accuracy,
1 - bfs_accuracy
)
) %>%
# What's the probability of *completing* BFS-online all the way through?
rowwise() %>%
mutate(
search_threshold = search_threshold,
p_complete_bfs = softmax(
option_values = c(search_threshold, bfs_visits),
option_chosen = 1,
temperature = 1
)
) %>%
ungroup() %>%
# Weigh BFS predictions accordingly
mutate(
p_give_up = 1 - p_complete_bfs,
predicted_correct = (p_complete_bfs * p_sub_choice_bfs) + (p_give_up * 1/2),
### Add lapse rate
# Dividing by 2 is because there are two options to choose from
# Therefore, when lapse rate = 1, this becomes chance = 1/2
predicted_correct = predicted_correct * (1-lapse_rate) + (lapse_rate/2)
) %>%
# Average over trials
group_by(study, sub_id, measurement_id, shortest_path) %>%
summarise(
empirical = mean(correct),
predicted = mean(predicted_correct),
.groups = "drop"
)
## Joining with `by = join_by(study, sub_id, measurement_id)`
## Joining with `by = join_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id)`
ppc_sr_matrix <- params %>%
filter(model == "sr") %>%
select(
study, sub_id, measurement_id, name = param_name, value = param_value
) %>%
pivot_wider() %>%
group_by(study, sub_id, measurement_id) %>%
nest() %>%
mutate(
sim_sr = map(
.x = data,
.f = ~build_rep_sr(
learning_data = simulated_paired_associates %>% filter(set %in% 1:100),
this_alpha = 0.1,
this_gamma = .x$sr_gamma,
bidirectional = TRUE
)
)
) %>%
unnest(sim_sr) %>%
unnest(data) %>%
ungroup()
ppc_sr_behavior <- bind_rows(nav_study1, nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2")) %>%
left_join(
ppc_sr_matrix %>%
rename(opt1_id = from, endpoint_id = to, opt1_sr = sr_value)
) %>%
left_join(
ppc_sr_matrix %>%
rename(opt2_id = from, endpoint_id = to, opt2_sr = sr_value)
) %>%
rowwise() %>%
mutate(
predicted_correct = softmax(
option_values = c(opt1_sr, opt2_sr) * 100,
option_chosen = if_else(correct_choice == opt1_id, 1, 2),
temperature = softmax_temperature,
use_inverse_temperature = TRUE,
lapse_rate = lapse_rate
)
) %>%
ungroup() %>%
# Fix trials when the softmax becomes undefined
mutate(
predicted_correct = case_when(
is.nan(predicted_correct) & (sub_choice == correct_choice) ~ 1,
is.nan(predicted_correct) & (sub_choice != correct_choice) ~ 0,
TRUE ~ predicted_correct
)
) %>%
# Average over trials
group_by(study, sub_id, measurement_id, shortest_path) %>%
summarise(
empirical = mean(correct),
predicted = mean(predicted_correct),
.groups = "drop"
)
## Joining with `by = join_by(study, sub_id, measurement_id, endpoint_id, opt1_id)`
## Joining with `by = join_by(study, sub_id, measurement_id, endpoint_id, opt2_id, sr_gamma,
## softmax_temperature, lapse_rate)`
Now that we have both PPCs, we can plot them side-by-side.
plot_ppc_day1 <- ppc_bfs %>%
rename(predicted_bfs = predicted) %>%
left_join(ppc_sr_behavior %>% rename(predicted_sr = predicted)) %>%
pivot_longer(
c(empirical, predicted_bfs, predicted_sr),
names_to = "agent", values_to = "accuracy"
) %>%
filter(measurement_id == "D1") %>%
ggplot(aes(x=shortest_path, y=accuracy)) +
theme_custom() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_point(
aes(color = agent),
alpha = 0.05,
position = position_jitterdodge(
jitter.width = 0.2, jitter.height = 0, dodge.width = 0.75, seed = 1
),
show.legend = FALSE
) +
stat_summary(
aes(color = agent), geom = "crossbar", fun = mean,
position = position_dodge(0.5)
) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
) +
scale_color_manual(
name = NULL,
values = c(
"empirical"="#fd8d3c",
"predicted_bfs"="#8c2d04",
"predicted_sr"="#bd0026"
),
labels = c("empirical"="Human", "predicted_bfs"="BFS", "predicted_sr"="SR")
) +
theme(legend.position = "bottom") +
ggtitle("Posterior predictive check")
## Joining with `by = join_by(study, sub_id, measurement_id, shortest_path,
## empirical)`
plot_ppc_day1

if (knitting) {
ggsave(
here("figures", str_c("ppc_day1", ".pdf")),
plot = plot_ppc_day1,
width = 8/2, height = 2.5,
units = "in", dpi = 300
)
}
Held-out trials
There were some trials where both Sources had equivalent shortest
path distances from the Target. We would expect that BFS would be
largely indifferent between the two Sources, but it is possible that
humans and/or the SR would make other predictions.
nav_study1_ties <- here("data", "clean-data", "study1_message_passing.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == TRUE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(
study = "Study 1",
measurement_id = str_c("D", measurement_id),
shortest_path = factor(shortest_path_given_opts)
) %>%
select(
study, sub_id, measurement_id, shortest_path,
startpoint_id, endpoint_id,
opt1_id, opt2_id,
correct_choice, sub_choice,
correct, rt
) %>%
filter(measurement_id %in% c("D1", "D2"))
nav_study2_ties <- here("data", "clean-data", "study2_message_passing.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == TRUE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(
study = "Study 2",
measurement_id = case_when(
network == "learned" ~ str_c("D", measurement_id),
network == "reevaluated" ~ "D2b"
),
shortest_path = factor(shortest_path_given_opts)
) %>%
select(
study, sub_id, measurement_id, shortest_path,
startpoint_id, endpoint_id,
opt1_id, opt2_id,
correct_choice, sub_choice,
correct, rt
) %>%
filter(measurement_id %in% c("D1", "D2"))
nav_study3_ties <- here("data", "clean-data", "study3_message_passing.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == TRUE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(
study = "Study 3",
measurement_id = case_when(
network == "reevaluated" ~ "D2b",
measurement_id == 1 ~ "D1",
measurement_id == 2 ~ "D1b",
measurement_id == 3 ~ "D2"
),
shortest_path = factor(shortest_path_given_opts)
) %>%
select(
study, sub_id, measurement_id, shortest_path,
startpoint_id, endpoint_id,
opt1_id, opt2_id,
correct_choice, sub_choice,
correct, rt
) %>%
filter(measurement_id %in% c("D1", "D2"))
tie_item_analysis_humans <- bind_rows(
nav_study1_ties, nav_study2_ties, nav_study3_ties
) %>%
filter(measurement_id == "D1") %>%
group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
summarise(
p_human_opt1 = mean(sub_choice == opt1_id),
.groups = "drop"
)
bfs_avg_accuracy_ties <- here("data", "bfs-sims", "bfs_sims_learned.csv") %>%
read_csv(show_col_types = FALSE) %>%
filter(
two_correct_options == TRUE,
shortest_path_given_opts == shortest_path_given_start_end
) %>%
mutate(shortest_path = factor(shortest_path_given_opts)) %>%
select(
shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id,
bfs_choice, bfs_correct_choice, bfs_n_visits_total
) %>%
group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
summarise(
p_bfs_opt1 = mean(bfs_choice == opt1_id),
bfs_visits = mean(bfs_n_visits_total),
.groups = "drop"
)
tie_item_analysis_bfs <- bind_rows(
nav_study1_ties, nav_study2_ties, nav_study3_ties
) %>%
filter(measurement_id == "D1") %>%
left_join(
params %>%
filter(model == "bfs") %>%
pivot_wider(names_from = param_name, values_from = param_value) %>%
select(study, sub_id, measurement_id, search_threshold, lapse_rate)
) %>%
left_join(bfs_avg_accuracy_ties) %>%
mutate(
p_sub_choice_bfs = if_else(
sub_choice == opt1_id,
p_bfs_opt1,
1 - p_bfs_opt1
)
) %>%
select(-p_bfs_opt1) %>%
# What's the probability of *completing* BFS-online all the way through?
rowwise() %>%
mutate(
search_threshold = search_threshold,
p_complete_bfs = softmax(
option_values = c(search_threshold, bfs_visits),
option_chosen = 1,
temperature = 1
)
) %>%
ungroup() %>%
# Weigh BFS predictions accordingly
mutate(
p_give_up = 1 - p_complete_bfs,
p_bfs_opt1 = (p_complete_bfs * p_sub_choice_bfs) + (p_give_up * 1/2),
### Add lapse rate
# Dividing by 2 is because there are two options to choose from
# Therefore, when lapse rate = 1, this becomes chance = 1/2
p_bfs_opt1 = p_bfs_opt1 * (1-lapse_rate) + (lapse_rate/2)
) %>%
# Average over trials
# group_by(shortest_path) %>%
group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
summarise(
p_bfs_opt1 = mean(p_bfs_opt1),
.groups = "drop"
)
## Joining with `by = join_by(study, sub_id, measurement_id)`
## Joining with `by = join_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id)`
tie_item_analysis_sr <- bind_rows(
nav_study1_ties, nav_study2_ties, nav_study3_ties
) %>%
filter(measurement_id == "D1") %>%
left_join(
ppc_sr_matrix %>%
select(
study, sub_id, measurement_id,
opt1_id = from,
endpoint_id = to,
opt1_sr = sr_value
)
) %>%
left_join(
ppc_sr_matrix %>%
select(
study, sub_id, measurement_id,
opt2_id = from,
endpoint_id = to,
opt2_sr = sr_value,
sr_gamma, softmax_temperature, lapse_rate
)
) %>%
rowwise() %>%
mutate(
p_sr_opt1 = softmax(
option_values = c(opt1_sr, opt2_sr) * 100,
option_chosen = 1,
temperature = softmax_temperature,
use_inverse_temperature = TRUE,
lapse_rate = lapse_rate
)
) %>%
group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
summarise(
p_sr_opt1 = mean(p_sr_opt1, na.rm = TRUE),
.groups = "drop"
)
## Joining with `by = join_by(study, sub_id, measurement_id, endpoint_id, opt1_id)`
## Joining with `by = join_by(study, sub_id, measurement_id, endpoint_id, opt2_id)`
plot_ties <- tie_item_analysis_humans %>%
left_join(tie_item_analysis_bfs) %>%
left_join(tie_item_analysis_sr) %>%
mutate(item = row_number(), item = factor(item)) %>%
pivot_longer(
starts_with("p_"),
names_to = "agent",
values_to = "p_choose_opt1"
) %>%
mutate(
agent = str_remove_all(agent, "p_|_opt1"),
agent = case_when(
agent == "human" ~ "Human",
agent == "sr" ~ "SR",
agent == "bfs" ~ "BFS",
),
agent = fct_relevel(agent, "Human", "SR")
) %>%
ggplot(aes(x=shortest_path, y=p_choose_opt1)) +
theme_custom() +
facet_wrap(~agent) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_point(alpha = 0.25) +
stat_summary(geom = "crossbar", fun = mean, color = "red") +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "p(Choose Source A > B)",
labels = scales::percent, breaks = seq(0, 1, 0.25)
) +
theme(
panel.grid = element_blank(),
legend.position = "bottom"
) +
ggtitle("Navigation problems with two correct answers")
## Joining with `by = join_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id)`
## Joining with `by = join_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id)`
plot_ties

if (knitting) {
ggsave(
here("figures", str_c("navigation_heldout_day1", ".pdf")),
plot = plot_ties,
width = 8/2, height = 2.5,
units = "in", dpi = 300
)
}
Does navigation improve after rest?
First, some descriptives…
bind_rows(nav_study1, nav_study2, nav_study3) %>%
group_by(measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
arrange(measurement_id, shortest_path) %>%
pivot_wider(
names_from = shortest_path, values_from = accuracy, names_prefix = "dist-"
) %>%
kbl(
caption = str_c(
"<center>", "Descriptive: Navigation accuracy", "</center>"
),
digits = 2
) %>%
kable_styling(bootstrap_options = c("responsive"))
Descriptive: Navigation accuracy
|
measurement_id
|
dist-2
|
dist-3
|
dist-4
|
|
D1
|
0.80
|
0.70
|
0.63
|
|
D1b
|
0.79
|
0.72
|
0.68
|
|
D2
|
0.82
|
0.75
|
0.71
|
|
D2b
|
0.81
|
0.72
|
0.62
|
Overnight rest
Now we want to see how navigation accuracy changes from day 1 to day
2. This pools across studies 2-3 (study 1 was only a one-day
experiment).
nav_day1_to_day2 <- bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2")) %>%
# Give every subject a distinct identifier
mutate(sub_id = str_c(study, " s", sub_id))
stats_nav_day2_dist2 <- nav_day1_to_day2 %>%
mutate(shortest_path = fct_relevel(shortest_path, "2")) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id) + (1 | study),
family = binomial,
data = .
)
stats_nav_day2_dist3 <- nav_day1_to_day2 %>%
mutate(shortest_path = fct_relevel(shortest_path, "3")) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id) + (1 | study),
family = binomial,
data = .
)
stats_nav_day2_dist4 <- nav_day1_to_day2 %>%
mutate(shortest_path = fct_relevel(shortest_path, "4")) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id) + (1 | study),
family = binomial,
data = .
)
map_dfr(
.x = list(
"dist-2" = stats_nav_day2_dist2,
"dist-3" = stats_nav_day2_dist3,
"dist-4" = stats_nav_day2_dist4
),
.f = ~tidy(.x, conf.int = TRUE),
.id = "ref_cat"
) %>%
check_significance() %>%
select(-c(ref_cat, effect, component)) %>%
kbl(
caption = str_c(
"<center>", "Navigation accuracy: Day 1 to Day 2", "</center>"
),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive")) %>%
pack_rows("Ref. Cat. dist-2", 1, 17) %>%
pack_rows("Ref. Cat. dist-3", 18, 34) %>%
pack_rows("Ref. Cat. dist-4", 35, 51)
Navigation accuracy: Day 1 to Day 2
|
group
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
conf.low
|
conf.high
|
sig
|
|
Ref. Cat. dist-2
|
|
NA
|
(Intercept)
|
1.765
|
0.114
|
15.476
|
0.000
|
1.541
|
1.988
|
***
|
|
NA
|
shortest_path3
|
-0.452
|
0.083
|
-5.452
|
0.000
|
-0.615
|
-0.290
|
***
|
|
NA
|
shortest_path4
|
-0.959
|
0.091
|
-10.554
|
0.000
|
-1.137
|
-0.781
|
***
|
|
NA
|
measurement_idD2
|
0.234
|
0.080
|
2.906
|
0.004
|
0.076
|
0.391
|
**
|
|
NA
|
shortest_path3:measurement_idD2
|
0.030
|
0.087
|
0.351
|
0.725
|
-0.139
|
0.200
|
|
|
NA
|
shortest_path4:measurement_idD2
|
0.196
|
0.083
|
2.356
|
0.018
|
0.033
|
0.359
|
|
|
sub_id
|
sd__(Intercept)
|
1.024
|
NA
|
NA
|
NA
|
0.858
|
1.223
|
|
|
sub_id
|
sd__shortest_path3
|
0.461
|
NA
|
NA
|
NA
|
0.334
|
0.635
|
|
|
sub_id
|
sd__shortest_path4
|
0.634
|
NA
|
NA
|
NA
|
0.512
|
0.785
|
|
|
sub_id
|
sd__measurement_idD2
|
0.483
|
NA
|
NA
|
NA
|
0.370
|
0.631
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
0.182
|
NA
|
NA
|
NA
|
-0.175
|
0.481
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.250
|
NA
|
NA
|
NA
|
-0.799
|
0.057
|
|
|
sub_id
|
cor__(Intercept).measurement_idD2
|
0.451
|
NA
|
NA
|
NA
|
-0.037
|
0.608
|
|
|
sub_id
|
cor__shortest_path3.shortest_path4
|
0.808
|
NA
|
NA
|
NA
|
0.311
|
0.873
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD2
|
0.466
|
NA
|
NA
|
NA
|
-0.116
|
0.837
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD2
|
0.007
|
NA
|
NA
|
NA
|
-0.520
|
0.712
|
|
|
study
|
sd__(Intercept)
|
0.000
|
NA
|
NA
|
NA
|
0.000
|
Inf
|
|
|
Ref. Cat. dist-3
|
|
NA
|
(Intercept)
|
1.312
|
0.134
|
9.810
|
0.000
|
1.050
|
1.574
|
***
|
|
NA
|
shortest_path2
|
0.452
|
0.083
|
5.452
|
0.000
|
0.290
|
0.615
|
***
|
|
NA
|
shortest_path4
|
-0.507
|
0.079
|
-6.411
|
0.000
|
-0.662
|
-0.352
|
***
|
|
NA
|
measurement_idD2
|
0.264
|
0.088
|
2.989
|
0.003
|
0.091
|
0.437
|
**
|
|
NA
|
shortest_path2:measurement_idD2
|
-0.030
|
0.087
|
-0.351
|
0.725
|
-0.200
|
0.139
|
|
|
NA
|
shortest_path4:measurement_idD2
|
0.166
|
0.090
|
1.833
|
0.067
|
-0.011
|
0.343
|
.
|
|
sub_id
|
sd__(Intercept)
|
1.197
|
NA
|
NA
|
NA
|
0.997
|
1.437
|
|
|
sub_id
|
sd__shortest_path2
|
0.461
|
NA
|
NA
|
NA
|
0.334
|
0.635
|
|
|
sub_id
|
sd__shortest_path4
|
0.377
|
NA
|
NA
|
NA
|
0.266
|
0.536
|
|
|
sub_id
|
sd__measurement_idD2
|
0.483
|
NA
|
NA
|
NA
|
0.370
|
0.631
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.541
|
NA
|
NA
|
NA
|
-0.743
|
-0.174
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.497
|
NA
|
NA
|
NA
|
-0.605
|
0.049
|
|
|
sub_id
|
cor__(Intercept).measurement_idD2
|
0.565
|
NA
|
NA
|
NA
|
-0.045
|
0.827
|
|
|
sub_id
|
cor__shortest_path2.shortest_path4
|
-0.137
|
NA
|
NA
|
NA
|
-0.002
|
0.234
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD2
|
-0.466
|
NA
|
NA
|
NA
|
-0.209
|
0.025
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD2
|
-0.557
|
NA
|
NA
|
NA
|
-0.093
|
0.401
|
|
|
study
|
sd__(Intercept)
|
0.000
|
NA
|
NA
|
NA
|
0.000
|
Inf
|
|
|
Ref. Cat. dist-4
|
|
NA
|
(Intercept)
|
0.805
|
0.118
|
6.827
|
0.000
|
0.574
|
1.037
|
***
|
|
NA
|
shortest_path2
|
0.959
|
0.091
|
10.554
|
0.000
|
0.781
|
1.137
|
***
|
|
NA
|
shortest_path3
|
0.507
|
0.079
|
6.411
|
0.000
|
0.352
|
0.662
|
***
|
|
NA
|
measurement_idD2
|
0.430
|
0.083
|
5.174
|
0.000
|
0.267
|
0.592
|
***
|
|
NA
|
shortest_path2:measurement_idD2
|
-0.196
|
0.083
|
-2.356
|
0.018
|
-0.359
|
-0.033
|
|
|
NA
|
shortest_path3:measurement_idD2
|
-0.166
|
0.090
|
-1.834
|
0.067
|
-0.343
|
0.011
|
.
|
|
sub_id
|
sd__(Intercept)
|
1.061
|
NA
|
NA
|
NA
|
0.883
|
1.276
|
|
|
sub_id
|
sd__shortest_path2
|
0.634
|
NA
|
NA
|
NA
|
0.512
|
0.785
|
|
|
sub_id
|
sd__shortest_path3
|
0.377
|
NA
|
NA
|
NA
|
0.265
|
0.536
|
|
|
sub_id
|
sd__measurement_idD2
|
0.483
|
NA
|
NA
|
NA
|
0.370
|
0.631
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.356
|
NA
|
NA
|
NA
|
-0.587
|
-0.038
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
0.205
|
NA
|
NA
|
NA
|
-0.465
|
0.400
|
|
|
sub_id
|
cor__(Intercept).measurement_idD2
|
0.439
|
NA
|
NA
|
NA
|
-0.096
|
0.450
|
|
|
sub_id
|
cor__shortest_path2.shortest_path3
|
0.695
|
NA
|
NA
|
NA
|
0.448
|
0.848
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD2
|
-0.007
|
NA
|
NA
|
NA
|
-0.118
|
0.214
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD2
|
0.557
|
NA
|
NA
|
NA
|
-0.497
|
0.628
|
|
|
study
|
sd__(Intercept)
|
0.000
|
NA
|
NA
|
NA
|
0.000
|
Inf
|
|
predict_nav_day1_to_day2 <- expand_grid(
measurement_id = c("D1", "D2"),
shortest_path = factor(2:4),
sub_id = NA, study = NA
) %>%
predict_glmmTMB(stats_nav_day2_dist2)
plot_nav_day1_to_day2 <- nav_day1_to_day2 %>%
group_by(sub_id, measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
ggplot(aes(x=shortest_path, y=accuracy, color=measurement_id)) +
theme_custom() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_dotplot(
aes(fill = measurement_id),
binwidth = 0.01,
binaxis = "y", stackdir = "center",
position = position_dodge(width = 0.75),
dotsize = 1, alpha = 0.5, color = NA,
show.legend = FALSE
) +
geom_pointrange(
aes(
x = shortest_path, y = fit,
ymin = fit - se.fit, ymax = fit + se.fit,
color = measurement_id
),
data = predict_nav_day1_to_day2, inherit.aes = FALSE, show.legend = FALSE,
position = position_dodge(width = 0.15), linewidth = 1
) +
geom_line(
aes(
x = shortest_path, y = fit,
group = measurement_id, color = measurement_id
),
data = predict_nav_day1_to_day2, inherit.aes = FALSE,
position = position_dodge(width = 0.15), linewidth = 1
) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
) +
scale_color_manual(
name = "Measurement",
values = c("D1"="#fa9fb5", "D2"="#7a0177"),
labels = c("D1"="Before overnight rest", "D2"="After overnight rest")
) +
scale_fill_manual(values = c("D1"="#fa9fb5", "D2"="#7a0177")) +
coord_cartesian(ylim = c(0.3, 1.1)) +
theme(legend.position = "bottom") +
ggtitle("Navigation after overnight rest")
plot_nav_day1_to_day2

if (knitting) {
ggsave(
here("figures", str_c("navigation_day1_to_day2", ".pdf")),
plot = plot_nav_day1_to_day2,
width = 4, height = 3,
units = "in", dpi = 300
)
}
Awake rest
Is a brief period of awake rest sufficient for improving navigation
accuracy?
stats_nav_awake_dist2 <- nav_study3 %>%
filter(measurement_id %in% c("D1", "D1b")) %>%
mutate(shortest_path = fct_relevel(shortest_path, "2")) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id),
family = binomial,
data = .
)
stats_nav_awake_dist3 <- nav_study3 %>%
filter(measurement_id %in% c("D1", "D1b")) %>%
mutate(shortest_path = fct_relevel(shortest_path, "3")) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id),
family = binomial,
data = .
)
stats_nav_awake_dist4 <- nav_study3 %>%
filter(measurement_id %in% c("D1", "D1b")) %>%
mutate(shortest_path = fct_relevel(shortest_path, "4")) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id),
family = binomial,
data = .
)
map_dfr(
.x = list(
"dist-2" = stats_nav_awake_dist2,
"dist-3" = stats_nav_awake_dist3,
"dist-4" = stats_nav_awake_dist4
),
.f = ~tidy(.x, conf.int = TRUE),
.id = "ref_cat"
) %>%
check_significance() %>%
select(-c(ref_cat, effect, component)) %>%
kbl(
caption = str_c("<center>", "Navigation accuracy: Awake Rest", "</center>"),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive")) %>%
pack_rows("Ref. Cat. dist-2", 1, 16) %>%
pack_rows("Ref. Cat. dist-3", 17, 32) %>%
pack_rows("Ref. Cat. dist-4", 33, 48)
Navigation accuracy: Awake Rest
|
group
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
conf.low
|
conf.high
|
sig
|
|
Ref. Cat. dist-2
|
|
NA
|
(Intercept)
|
1.840
|
0.193
|
9.542
|
0.000
|
1.462
|
2.218
|
***
|
|
NA
|
shortest_path3
|
-0.424
|
0.129
|
-3.290
|
0.001
|
-0.677
|
-0.171
|
**
|
|
NA
|
shortest_path4
|
-0.936
|
0.134
|
-6.972
|
0.000
|
-1.199
|
-0.673
|
***
|
|
NA
|
measurement_idD1b
|
-0.058
|
0.122
|
-0.475
|
0.635
|
-0.296
|
0.181
|
|
|
NA
|
shortest_path3:measurement_idD1b
|
0.022
|
0.123
|
0.175
|
0.861
|
-0.220
|
0.263
|
|
|
NA
|
shortest_path4:measurement_idD1b
|
0.244
|
0.119
|
2.060
|
0.039
|
0.012
|
0.477
|
|
|
sub_id
|
sd__(Intercept)
|
1.216
|
NA
|
NA
|
NA
|
0.947
|
1.561
|
|
|
sub_id
|
sd__shortest_path3
|
0.518
|
NA
|
NA
|
NA
|
0.352
|
0.764
|
|
|
sub_id
|
sd__shortest_path4
|
0.632
|
NA
|
NA
|
NA
|
0.465
|
0.859
|
|
|
sub_id
|
sd__measurement_idD1b
|
0.544
|
NA
|
NA
|
NA
|
0.395
|
0.751
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
-0.019
|
NA
|
NA
|
NA
|
-0.462
|
0.435
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.262
|
NA
|
NA
|
NA
|
-0.840
|
0.127
|
|
|
sub_id
|
cor__(Intercept).measurement_idD1b
|
0.045
|
NA
|
NA
|
NA
|
-0.264
|
0.378
|
|
|
sub_id
|
cor__shortest_path3.shortest_path4
|
0.845
|
NA
|
NA
|
NA
|
0.444
|
0.913
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD1b
|
0.191
|
NA
|
NA
|
NA
|
-0.062
|
0.647
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD1b
|
-0.024
|
NA
|
NA
|
NA
|
-0.184
|
0.641
|
|
|
Ref. Cat. dist-3
|
|
NA
|
(Intercept)
|
1.416
|
0.211
|
6.714
|
0.000
|
1.003
|
1.829
|
***
|
|
NA
|
shortest_path2
|
0.424
|
0.129
|
3.290
|
0.001
|
0.171
|
0.677
|
**
|
|
NA
|
shortest_path4
|
-0.512
|
0.116
|
-4.427
|
0.000
|
-0.738
|
-0.285
|
***
|
|
NA
|
measurement_idD1b
|
-0.036
|
0.133
|
-0.273
|
0.785
|
-0.296
|
0.224
|
|
|
NA
|
shortest_path2:measurement_idD1b
|
-0.022
|
0.123
|
-0.175
|
0.861
|
-0.263
|
0.220
|
|
|
NA
|
shortest_path4:measurement_idD1b
|
0.223
|
0.129
|
1.722
|
0.085
|
-0.031
|
0.476
|
.
|
|
sub_id
|
sd__(Intercept)
|
1.313
|
NA
|
NA
|
NA
|
1.010
|
1.705
|
|
|
sub_id
|
sd__shortest_path2
|
0.518
|
NA
|
NA
|
NA
|
0.352
|
0.764
|
|
|
sub_id
|
sd__shortest_path4
|
0.338
|
NA
|
NA
|
NA
|
0.190
|
0.603
|
|
|
sub_id
|
sd__measurement_idD1b
|
0.544
|
NA
|
NA
|
NA
|
0.395
|
0.751
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.377
|
NA
|
NA
|
NA
|
-0.704
|
0.175
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.408
|
NA
|
NA
|
NA
|
-0.681
|
0.342
|
|
|
sub_id
|
cor__(Intercept).measurement_idD1b
|
0.117
|
NA
|
NA
|
NA
|
-0.224
|
0.497
|
|
|
sub_id
|
cor__shortest_path2.shortest_path4
|
-0.046
|
NA
|
NA
|
NA
|
0.106
|
0.502
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD1b
|
-0.191
|
NA
|
NA
|
NA
|
-0.124
|
0.355
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD1b
|
-0.337
|
NA
|
NA
|
NA
|
0.009
|
0.560
|
|
|
Ref. Cat. dist-4
|
|
NA
|
(Intercept)
|
0.904
|
0.193
|
4.684
|
0.000
|
0.526
|
1.283
|
***
|
|
NA
|
shortest_path2
|
0.936
|
0.134
|
6.971
|
0.000
|
0.673
|
1.199
|
***
|
|
NA
|
shortest_path3
|
0.512
|
0.116
|
4.427
|
0.000
|
0.285
|
0.738
|
***
|
|
NA
|
measurement_idD1b
|
0.187
|
0.126
|
1.476
|
0.140
|
-0.061
|
0.434
|
|
|
NA
|
shortest_path2:measurement_idD1b
|
-0.244
|
0.119
|
-2.060
|
0.039
|
-0.477
|
-0.012
|
|
|
NA
|
shortest_path3:measurement_idD1b
|
-0.223
|
0.129
|
-1.722
|
0.085
|
-0.476
|
0.031
|
.
|
|
sub_id
|
sd__(Intercept)
|
1.214
|
NA
|
NA
|
NA
|
0.935
|
1.577
|
|
|
sub_id
|
sd__shortest_path2
|
0.632
|
NA
|
NA
|
NA
|
0.465
|
0.859
|
|
|
sub_id
|
sd__shortest_path3
|
0.338
|
NA
|
NA
|
NA
|
0.190
|
0.603
|
|
|
sub_id
|
sd__measurement_idD1b
|
0.544
|
NA
|
NA
|
NA
|
0.395
|
0.751
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.258
|
NA
|
NA
|
NA
|
-0.602
|
0.216
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
0.163
|
NA
|
NA
|
NA
|
-0.621
|
0.521
|
|
|
sub_id
|
cor__(Intercept).measurement_idD1b
|
0.033
|
NA
|
NA
|
NA
|
-0.386
|
0.307
|
|
|
sub_id
|
cor__shortest_path2.shortest_path3
|
0.573
|
NA
|
NA
|
NA
|
0.343
|
0.835
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD1b
|
0.024
|
NA
|
NA
|
NA
|
-0.041
|
0.338
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD1b
|
0.337
|
NA
|
NA
|
NA
|
-0.003
|
0.674
|
|
predict_nav_awake <- expand_grid(
measurement_id = c("D1", "D1b"),
shortest_path = factor(2:4),
sub_id = NA, study = NA
) %>%
predict_glmmTMB(stats_nav_awake_dist2)
plot_nav_awake <- nav_study3 %>%
filter(measurement_id %in% c("D1", "D1b")) %>%
group_by(sub_id, measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
ggplot(aes(x=shortest_path, y=accuracy, color=measurement_id)) +
theme_custom() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_dotplot(
aes(fill = measurement_id),
binwidth = 0.01,
binaxis = "y", stackdir = "center",
position = position_dodge(width = 0.75),
dotsize = 1, alpha = 0.5, color = NA,
show.legend = FALSE
) +
geom_pointrange(
aes(
x = shortest_path, y = fit,
ymin = fit - se.fit, ymax = fit + se.fit,
color = measurement_id
),
data = predict_nav_awake, inherit.aes = FALSE, show.legend = FALSE,
position = position_dodge(width = 0.15), linewidth = 1
) +
geom_line(
aes(
x = shortest_path, y = fit,
group = measurement_id, color = measurement_id
),
data = predict_nav_awake, inherit.aes = FALSE,
position = position_dodge(width = 0.15), linewidth = 1
) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
) +
scale_color_manual(
name = "Measurement",
values = c("D1"="#fa9fb5", "D1b"="#2c7fb8"),
labels = c("D1"="Before overnight rest", "D1b"="After awake rest")
) +
scale_fill_manual(values = c("D1"="#fa9fb5", "D1b"="#2c7fb8")) +
coord_cartesian(ylim = c(0.3, 1.1)) +
theme(legend.position = "bottom") +
ggtitle("Navigation after awake rest")
plot_nav_awake

if (knitting) {
ggsave(
here("figures", str_c("navigation_awake_rest", ".pdf")),
plot = plot_nav_awake,
width = 4, height = 3,
units = "in", dpi = 300
)
}
SR replay simulation
Before starting to do any simulation, we first want to know how much
replay an agent can fit into different periods of time. Based on past
research measuring neural replay events, we’ll assume that it takes
about 50ms for the brain to replay a single item from a sequence. This
will let us calculate the total number of items that could be replayed
in each epoch. To simplify the process of actually running the
simulation, we’ll convert this quantity into the number of “sets” that
can be replayed, where a single “set” consists of the total number of
relationships in the network (i.e., the number of undirected edges = 17,
meaning that each set consists of 34 observations because each set
contains observations of both A->B and B->A).
tibble(minutes_for_replay = c(1, 5, 15, 30, 60, 120)) %>%
mutate(
n_items = minutes_for_replay / (0.05 / 60),
n_sets = n_items / 34
) %>%
kbl(
caption = str_c("<center>", "SR replay time", "</center>"),
digits = 2
) %>%
kable_styling(bootstrap_options = c("responsive"))
SR replay time
|
minutes_for_replay
|
n_items
|
n_sets
|
|
1
|
1200
|
35.29
|
|
5
|
6000
|
176.47
|
|
15
|
18000
|
529.41
|
|
30
|
36000
|
1058.82
|
|
60
|
72000
|
2117.65
|
|
120
|
144000
|
4235.29
|
sim_replay_0_min <- simulated_paired_associates %>%
filter(set <= 6) %>%
simulate_sr() %>%
mutate(replay_time = 0)
sim_replay_1_min <- simulated_paired_associates %>%
filter(set <= 35 + 6) %>%
simulate_sr() %>%
mutate(replay_time = 1)
sim_replay_5_min <- simulated_paired_associates %>%
filter(set <= 176 + 6) %>%
simulate_sr() %>%
mutate(replay_time = 5)
sim_replay_60_min <- simulated_paired_associates %>%
filter(set <= 2117 + 6) %>%
simulate_sr() %>%
mutate(replay_time = 60)
plot_sim_sr_replay <- bind_rows(
join_sr(triallist_nav_learned, sim_replay_0_min),
join_sr(triallist_nav_learned, sim_replay_1_min),
join_sr(triallist_nav_learned, sim_replay_5_min),
join_sr(triallist_nav_learned, sim_replay_60_min)
) %>%
# Feed values through softmax
mutate(across(c(opt1_sr, opt2_sr), ~.x * 100)) %>%
expand_grid(temperature = 1) %>%
rowwise() %>%
mutate(
p_correct = softmax(
option_values = c(opt1_sr, opt2_sr),
option_chosen = if_else(correct_choice == opt1_id, 1, 2),
temperature = temperature,
use_inverse_temperature = TRUE
)
) %>%
ungroup() %>%
# For plotting
mutate(
gamma = factor(gamma),
temperature = str_pad(temperature, width = 2, side = "left"),
temperature = str_c(unicode_greek["tau"], " = ", temperature),
replay_time = str_pad(replay_time, width = 2, side = "left"),
replay_time = case_when(
str_detect(replay_time, "1$") ~ str_c(replay_time, " minute of replay"),
replay_time == " 0" ~ "No replay",
TRUE ~ str_c(replay_time, " minutes of replay")
),
replay_time = fct_relevel(replay_time, "No replay")
) %>%
group_by(replay_time, gamma, temperature, shortest_path) %>%
summarise(p_correct = mean(p_correct), .groups = "drop") %>%
# Plot
ggplot(aes(x=shortest_path, y=p_correct, color=gamma)) +
theme_custom() +
facet_grid(cols = vars(replay_time), rows = vars(temperature)) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_line(aes(color = gamma, group = gamma), linewidth = 0.8) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(name = "Accuracy", labels = scales::percent) +
scale_color_viridis_d(
name = str_c(unicode_greek["gamma"], " = "), option = "turbo", end = 0.9
) +
guides(color = guide_legend(byrow = TRUE, nrow = 1)) +
coord_cartesian(ylim = c(0.5, 1)) +
theme(legend.position = "bottom") +
ggtitle("Simulated effects of SR replay on navigation")
## Joining with `by = join_by(endpoint_id, opt1_id)`
## Warning in left_join(., simulated_sr %>% rename(opt1_sr = sr_value, opt1_id = from, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 45 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this
## warning.
## Joining with `by = join_by(endpoint_id, opt2_id, gamma, replay_time)`
## Joining with `by = join_by(endpoint_id, opt1_id)`
## Warning in left_join(., simulated_sr %>% rename(opt1_sr = sr_value, opt1_id = from, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 45 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this
## warning.
## Joining with `by = join_by(endpoint_id, opt2_id, gamma, replay_time)`
## Joining with `by = join_by(endpoint_id, opt1_id)`
## Warning in left_join(., simulated_sr %>% rename(opt1_sr = sr_value, opt1_id = from, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 45 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this
## warning.
## Joining with `by = join_by(endpoint_id, opt2_id, gamma, replay_time)`
## Joining with `by = join_by(endpoint_id, opt1_id)`
## Warning in left_join(., simulated_sr %>% rename(opt1_sr = sr_value, opt1_id = from, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 45 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this
## warning.
## Joining with `by = join_by(endpoint_id, opt2_id, gamma, replay_time)`
plot_sim_sr_replay

if (knitting) {
ggsave(
here("figures", str_c("simulated_replay", ".pdf")),
plot = plot_sim_sr_replay,
width = 7, height = 2.5,
units = "in", dpi = 300,
device = cairo_pdf
)
}
PPC before/after rest
plot_ppc_day1_to_day2 <- ppc_sr_behavior %>%
filter(study %in% c("Study 2", "Study 3")) %>%
mutate(
measurement_id = case_when(
measurement_id == "D1" ~ "Before rest",
measurement_id == "D2" ~ "After rest"
),
measurement_id = fct_relevel(measurement_id, "Before rest")
) %>%
pivot_longer(c(empirical, predicted)) %>%
ggplot(aes(x=shortest_path, y=value)) +
theme_custom() +
facet_wrap(~measurement_id) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_point(
aes(color = name),
alpha = 0.05,
position = position_jitterdodge(
jitter.width = 0.2, jitter.height = 0, dodge.width = 0.5, seed = 1
),
show.legend = FALSE
) +
stat_summary(
aes(color = name), geom = "crossbar", fun = mean,
position = position_dodge(0.5)
) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
) +
scale_color_manual(
name = NULL,
values = c("empirical"="#fd8d3c", "predicted"="#bd0026"),
labels = c("empirical"="Human", "predicted"="SR")
) +
theme(legend.position = "bottom") +
ggtitle("Posterior predictive check")
plot_ppc_day1_to_day2

if (knitting) {
ggsave(
here("figures", str_c("ppc_day2", ".pdf")),
plot = plot_ppc_day1_to_day2,
width = 8/3, height = 2.5,
units = "in", dpi = 300
)
}
Relating model parameters to navigation
Does estimated gamma significantly increase after overnight rest?
params %>%
filter(
study %in% c("Study 2", "Study 3"),
measurement_id %in% c("D1", "D2"),
model == "sr",
param_name == "sr_gamma"
) %>%
select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
group_by(measurement_id) %>%
summarise(median_gamma = median(sr_gamma)) %>%
kbl(
caption = str_c(
"<center>", "Median SR gamma before/after overnight rest", "</center>"
),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive"))
Median SR gamma before/after overnight rest
|
measurement_id
|
median_gamma
|
|
D1
|
0.512
|
|
D2
|
0.659
|
params %>%
filter(
study %in% c("Study 2", "Study 3"),
measurement_id %in% c("D1", "D2"),
model == "sr",
param_name == "sr_gamma"
) %>%
select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
pivot_wider(names_from = measurement_id, values_from = sr_gamma) %>%
with(
wilcox.test(
D2, D1, alternative = "greater", paired = TRUE, conf.int = TRUE
)
) %>%
tidy() %>%
kbl(
caption = str_c(
"<center>", "Increase in SR gamma after overnight rest", "</center>"
),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive"))
Increase in SR gamma after overnight rest
|
estimate
|
statistic
|
p.value
|
conf.low
|
conf.high
|
method
|
alternative
|
|
0.064
|
2876
|
0.023
|
0.009
|
Inf
|
Wilcoxon signed rank test with continuity correction
|
greater
|
plot_gamma_change <- params %>%
filter(
study %in% c("Study 2", "Study 3"),
measurement_id %in% c("D1", "D2"),
model == "sr",
param_name == "sr_gamma"
) %>%
select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
ggplot(aes(x=measurement_id, y=sr_gamma, color=measurement_id)) +
theme_custom() +
geom_point(
position = position_jitterdodge(
jitter.width = 0.25, jitter.height = 0, dodge.width = 0.5, seed = 1
),
alpha = 0.25,
show.legend = FALSE
) +
stat_summary(
geom = "crossbar", fun = median, position = "dodge", show.legend = FALSE
) +
scale_x_discrete(
name = "Measurement",
labels = c("D1"="Before rest", "D2"="After rest")
) +
scale_y_continuous(
name = str_c("Estimated ", unicode_greek["gamma"]),
breaks = seq(0, 1, 0.25)
) +
scale_color_manual(
name = "Measurement",
values = c("D1"="#fa9fb5", "D2"="#7a0177"),
labels = c("D1"="Before rest", "D2"="After rest")
) +
coord_cartesian(ylim = c(0, 1.1)) +
theme(legend.position = "bottom") +
ggtitle(
str_c(
unicode_greek["Delta"], unicode_greek["gamma"], " after overnight rest"
)
)
plot_gamma_change

if (knitting) {
ggsave(
here("figures", str_c("gamma_change", ".pdf")),
plot = plot_gamma_change,
width = 8/3, height = 2.5,
units = "in", dpi = 300,
device = cairo_pdf
)
}
Are changes in estimated gamma related to changes in navigation
behaviors?
bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2")) %>%
group_by(study, sub_id, measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
pivot_wider(names_from = measurement_id, values_from = accuracy) %>%
mutate(delta_accuracy = D2 - D1) %>%
select(-c(D1, D2)) %>%
#
left_join(
params %>%
filter(
study %in% c("Study 2", "Study 3"),
measurement_id %in% c("D1", "D2"),
model == "sr",
param_name == "sr_gamma"
) %>%
select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
pivot_wider(names_from = measurement_id, values_from = sr_gamma) %>%
mutate(delta_sr = D2 - D1) %>%
select(-c(D1, D2)),
by = c("study", "sub_id")
) %>%
group_by(shortest_path) %>%
nest() %>%
mutate(
test = map(
.x = data,
.f = ~with(
.x,
cor.test(
delta_accuracy, delta_sr,
method = "spearman", exact = FALSE, alternative = "greater"
)
) %>% tidy()
)
) %>%
unnest(test) %>%
ungroup() %>%
select(-data) %>%
kbl(
caption = str_c(
"<center>", "∆ Accuracy ~ ∆ SR gamma (after overnight rest)", "</center>"
),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive"))
∆ Accuracy ~ ∆ SR gamma (after overnight rest)
|
shortest_path
|
estimate
|
statistic
|
p.value
|
method
|
alternative
|
|
2
|
-0.180
|
173977.52
|
0.960
|
Spearman’s rank correlation rho
|
greater
|
|
3
|
0.205
|
117163.09
|
0.022
|
Spearman’s rank correlation rho
|
greater
|
|
4
|
0.453
|
80620.94
|
0.000
|
Spearman’s rank correlation rho
|
greater
|
plot_gamma_accuracy_change <- bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2")) %>%
group_by(study, sub_id, measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
pivot_wider(names_from = measurement_id, values_from = accuracy) %>%
mutate(delta_accuracy = D2 - D1) %>%
select(-c(D1, D2)) %>%
#
left_join(
params %>%
filter(
study %in% c("Study 2", "Study 3"),
measurement_id %in% c("D1", "D2"),
model == "sr",
param_name == "sr_gamma"
) %>%
select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
pivot_wider(names_from = measurement_id, values_from = sr_gamma) %>%
mutate(delta_sr = D2 - D1) %>%
select(-c(D1, D2)),
by = c("study", "sub_id")
) %>%
ggplot(aes(x=delta_sr, y=delta_accuracy, color=shortest_path)) +
theme_custom() +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_vline(xintercept = 0, linetype = "dashed") +
geom_point(alpha = 0.25, show.legend = FALSE) +
geom_smooth(method = "lm", se = FALSE, linewidth = 1.5) +
scale_x_continuous(name = str_c("Change in ", unicode_greek["gamma"])) +
scale_y_continuous(name = "Change in accuracy") +
scale_color_manual(
name = "Shortest path distance",
values = c("#88CCEE", "#CC6677", "#DDCC77")
) +
coord_cartesian(xlim = c(-1, 1.1)) +
theme(legend.position = "bottom") +
ggtitle(
str_c(
unicode_greek["Delta"], "Navigation ~ ",
unicode_greek["Delta"], unicode_greek["gamma"]
)
)
plot_gamma_accuracy_change
## `geom_smooth()` using formula = 'y ~ x'

if (knitting) {
suppressWarnings(
ggsave(
here("figures", str_c("gamma_accuracy_change", ".pdf")),
plot = plot_gamma_accuracy_change,
width = 8/3, height = 2.5,
units = "in", dpi = 300
)
)
ggsave(
here("figures", str_c("gamma_accuracy_change_cairo", ".pdf")),
plot = plot_gamma_accuracy_change,
width = 8/3, height = 2.5,
units = "in", dpi = 300,
device = cairo_pdf
)
}
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Evidence of cached representation
Here, we’re trying to get a sense for what is being cached, and
trying to see if there’s evidence that caching (as opposed to
model-based planning) is the primary driver of navigation improvement
after overnight rest.
Simulation visualization
butterfly_layout <- create_layout(g, layout = "stress")
plot_network_with_sr <- sim_sr_matrix_asymptotic %>%
mutate(
from_sorted = if_else(from < to, from, to),
to_sorted = if_else(from < to, to, from)
) %>%
group_by(gamma, from = from_sorted, to = to_sorted) %>%
summarise(sr_value = mean(sr_value)) %>%
filter(round(gamma, 1) %in% c(0.1, 0.5, 0.9)) %>%
filter(sr_value > 0.05) %>%
left_join(adjlist) %>%
filter(from < to) %>%
mutate(
edge = factor(edge),
gamma = str_c(unicode_greek["gamma"], " = ", gamma)
) %>%
tbl_graph(edges = ., directed = FALSE) %>%
mutate(name = row_number()) %>%
ggraph("manual", x=butterfly_layout$x, y=butterfly_layout$y) +
theme_network() +
facet_edges(~gamma, ncol = 1) +
geom_edge_link(aes(alpha = sr_value, color = edge)) +
geom_node_label(aes(label = name)) +
scale_edge_color_manual(
name = NULL,
values = c("0"="red", "1"="black"),
labels = c("0"="Inferred connections", "1"="Observed connections")
) +
scale_edge_alpha(name = "p(Target | Source)") +
theme(
legend.position = "bottom",
legend.box = "vertical",
strip.background = element_blank(),
strip.text = element_text(size = 13)
) +
ggtitle("Cognitive maps predicted by SR")
## `summarise()` has grouped output by 'gamma', 'from'. You can override using the `.groups` argument.
## Joining with `by = join_by(from, to)`
plot_network_with_sr

if (knitting) {
suppressWarnings(
ggsave(
here("figures", str_c("network_with_sr", ".pdf")),
plot = plot_network_with_sr,
width = 3.5, height = 6,
units = "in", dpi = 300
)
)
ggsave(
here("figures", str_c("network_with_sr_cairo", ".pdf")),
plot = plot_network_with_sr,
width = 3.5, height = 6,
units = "in", dpi = 300,
device = cairo_pdf
)
}
Transition reevaluation
Following transition reevaluation, a caching account predicts that
people’s navigation should get worse relative to their performance after
overnight rest. In contrast, a planning account predicts that people’s
navigation should not be greatly impacted by a relatively small set of
changes.
stats_nav_tr_dist2 <- bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
mutate(
measurement_id = fct_relevel(measurement_id, "D2b"),
shortest_path = fct_relevel(shortest_path, "2")
) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id) + (1 | study),
family = binomial,
data = .
)
stats_nav_tr_dist3 <- bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
mutate(
measurement_id = fct_relevel(measurement_id, "D2b"),
shortest_path = fct_relevel(shortest_path, "3")
) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id) + (1 | study),
family = binomial,
data = .
)
stats_nav_tr_dist4 <- bind_rows(nav_study2, nav_study3) %>%
filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
mutate(
measurement_id = fct_relevel(measurement_id, "D2b"),
shortest_path = fct_relevel(shortest_path, "4")
) %>%
glmmTMB(
correct ~ shortest_path * measurement_id +
(1 + shortest_path + measurement_id | sub_id) + (1 | study),
family = binomial,
data = .
)
map_dfr(
.x = list(
"dist-2" = stats_nav_tr_dist2,
"dist-3" = stats_nav_tr_dist3,
"dist-4" = stats_nav_tr_dist4
),
.f = ~tidy(.x, conf.int = TRUE),
.id = "ref_cat"
) %>%
check_significance() %>%
select(-c(ref_cat, effect, component)) %>%
kbl(
caption = str_c(
"<center>", "Navigation accuracy: Transition Reevaluation", "</center>"
),
digits = 3
) %>%
kable_styling(bootstrap_options = c("responsive")) %>%
pack_rows("Ref. Cat. dist-2", 1, 25) %>%
pack_rows("Ref. Cat. dist-3", 26, 50) %>%
pack_rows("Ref. Cat. dist-4", 51, 75)
Navigation accuracy: Transition Reevaluation
|
group
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
conf.low
|
conf.high
|
sig
|
|
Ref. Cat. dist-2
|
|
NA
|
(Intercept)
|
1.600
|
0.110
|
14.558
|
0.000
|
1.385
|
1.815
|
***
|
|
NA
|
shortest_path3
|
-0.536
|
0.075
|
-7.183
|
0.000
|
-0.682
|
-0.390
|
***
|
|
NA
|
shortest_path4
|
-1.029
|
0.080
|
-12.837
|
0.000
|
-1.187
|
-0.872
|
***
|
|
NA
|
measurement_idD1
|
0.038
|
0.066
|
0.584
|
0.559
|
-0.091
|
0.168
|
|
|
NA
|
measurement_idD2
|
0.181
|
0.068
|
2.657
|
0.008
|
0.048
|
0.315
|
**
|
|
NA
|
shortest_path3:measurement_idD1
|
0.052
|
0.077
|
0.672
|
0.502
|
-0.099
|
0.203
|
|
|
NA
|
shortest_path4:measurement_idD1
|
0.088
|
0.074
|
1.183
|
0.237
|
-0.058
|
0.234
|
|
|
NA
|
shortest_path3:measurement_idD2
|
0.071
|
0.079
|
0.902
|
0.367
|
-0.083
|
0.225
|
|
|
NA
|
shortest_path4:measurement_idD2
|
0.287
|
0.076
|
3.765
|
0.000
|
0.137
|
0.436
|
***
|
|
sub_id
|
sd__(Intercept)
|
0.728
|
NA
|
NA
|
NA
|
0.583
|
0.910
|
|
|
sub_id
|
sd__shortest_path3
|
0.369
|
NA
|
NA
|
NA
|
0.267
|
0.509
|
|
|
sub_id
|
sd__shortest_path4
|
0.437
|
NA
|
NA
|
NA
|
0.339
|
0.564
|
|
|
sub_id
|
sd__measurement_idD1
|
0.275
|
NA
|
NA
|
NA
|
0.193
|
0.391
|
|
|
sub_id
|
sd__measurement_idD2
|
0.285
|
NA
|
NA
|
NA
|
0.200
|
0.407
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
0.055
|
NA
|
NA
|
NA
|
-0.307
|
0.397
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.353
|
NA
|
NA
|
NA
|
-0.936
|
0.071
|
|
|
sub_id
|
cor__(Intercept).measurement_idD1
|
-0.296
|
NA
|
NA
|
NA
|
-0.440
|
0.119
|
|
|
sub_id
|
cor__(Intercept).measurement_idD2
|
0.414
|
NA
|
NA
|
NA
|
-0.036
|
0.488
|
|
|
sub_id
|
cor__shortest_path3.shortest_path4
|
0.865
|
NA
|
NA
|
NA
|
0.194
|
0.932
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD1
|
-0.181
|
NA
|
NA
|
NA
|
-0.216
|
0.273
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD2
|
0.171
|
NA
|
NA
|
NA
|
-0.246
|
0.510
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD1
|
-0.103
|
NA
|
NA
|
NA
|
0.245
|
0.333
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD2
|
-0.044
|
NA
|
NA
|
NA
|
-0.188
|
0.433
|
|
|
sub_id
|
cor__measurement_idD1.measurement_idD2
|
0.356
|
NA
|
NA
|
NA
|
0.560
|
0.808
|
|
|
study
|
sd__(Intercept)
|
0.028
|
NA
|
NA
|
NA
|
0.004
|
0.181
|
|
|
Ref. Cat. dist-3
|
|
NA
|
(Intercept)
|
1.064
|
0.124
|
8.567
|
0.000
|
0.821
|
1.307
|
***
|
|
NA
|
shortest_path2
|
0.536
|
0.075
|
7.183
|
0.000
|
0.390
|
0.682
|
***
|
|
NA
|
shortest_path4
|
-0.493
|
0.061
|
-8.036
|
0.000
|
-0.614
|
-0.373
|
***
|
|
NA
|
measurement_idD1
|
0.090
|
0.071
|
1.277
|
0.201
|
-0.048
|
0.229
|
|
|
NA
|
measurement_idD2
|
0.252
|
0.073
|
3.446
|
0.001
|
0.109
|
0.396
|
***
|
|
NA
|
shortest_path2:measurement_idD1
|
-0.052
|
0.077
|
-0.672
|
0.501
|
-0.203
|
0.099
|
|
|
NA
|
shortest_path4:measurement_idD1
|
0.036
|
0.078
|
0.459
|
0.646
|
-0.118
|
0.190
|
|
|
NA
|
shortest_path2:measurement_idD2
|
-0.071
|
0.079
|
-0.902
|
0.367
|
-0.225
|
0.083
|
|
|
NA
|
shortest_path4:measurement_idD2
|
0.216
|
0.080
|
2.684
|
0.007
|
0.058
|
0.374
|
**
|
|
sub_id
|
sd__(Intercept)
|
0.834
|
NA
|
NA
|
NA
|
0.664
|
1.048
|
|
|
sub_id
|
sd__shortest_path2
|
0.369
|
NA
|
NA
|
NA
|
0.267
|
0.509
|
|
|
sub_id
|
sd__shortest_path4
|
0.220
|
NA
|
NA
|
NA
|
0.137
|
0.353
|
|
|
sub_id
|
sd__measurement_idD1
|
0.275
|
NA
|
NA
|
NA
|
0.193
|
0.391
|
|
|
sub_id
|
sd__measurement_idD2
|
0.285
|
NA
|
NA
|
NA
|
0.200
|
0.407
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.490
|
NA
|
NA
|
NA
|
-0.717
|
-0.095
|
|
|
sub_id
|
cor__(Intercept).shortest_path4
|
-0.675
|
NA
|
NA
|
NA
|
-0.753
|
0.183
|
|
|
sub_id
|
cor__(Intercept).measurement_idD1
|
-0.338
|
NA
|
NA
|
NA
|
-0.491
|
0.099
|
|
|
sub_id
|
cor__(Intercept).measurement_idD2
|
0.437
|
NA
|
NA
|
NA
|
-0.051
|
0.528
|
|
|
sub_id
|
cor__shortest_path2.shortest_path4
|
-0.042
|
NA
|
NA
|
NA
|
0.134
|
0.401
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD1
|
0.181
|
NA
|
NA
|
NA
|
0.180
|
0.357
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD2
|
-0.172
|
NA
|
NA
|
NA
|
-0.180
|
0.211
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD1
|
0.099
|
NA
|
NA
|
NA
|
0.324
|
0.614
|
|
|
sub_id
|
cor__shortest_path4.measurement_idD2
|
-0.376
|
NA
|
NA
|
NA
|
-0.005
|
0.537
|
|
|
sub_id
|
cor__measurement_idD1.measurement_idD2
|
0.356
|
NA
|
NA
|
NA
|
0.549
|
0.793
|
|
|
study
|
sd__(Intercept)
|
0.028
|
NA
|
NA
|
NA
|
0.004
|
0.181
|
|
|
Ref. Cat. dist-4
|
|
NA
|
(Intercept)
|
0.571
|
0.106
|
5.360
|
0.000
|
0.362
|
0.779
|
***
|
|
NA
|
shortest_path2
|
1.029
|
0.080
|
12.836
|
0.000
|
0.872
|
1.187
|
***
|
|
NA
|
shortest_path3
|
0.493
|
0.061
|
8.036
|
0.000
|
0.373
|
0.614
|
***
|
|
NA
|
measurement_idD1
|
0.126
|
0.067
|
1.898
|
0.058
|
-0.004
|
0.257
|
.
|
|
NA
|
measurement_idD2
|
0.468
|
0.070
|
6.722
|
0.000
|
0.332
|
0.605
|
***
|
|
NA
|
shortest_path2:measurement_idD1
|
-0.088
|
0.074
|
-1.183
|
0.237
|
-0.234
|
0.058
|
|
|
NA
|
shortest_path3:measurement_idD1
|
-0.036
|
0.078
|
-0.459
|
0.646
|
-0.190
|
0.118
|
|
|
NA
|
shortest_path2:measurement_idD2
|
-0.287
|
0.076
|
-3.765
|
0.000
|
-0.436
|
-0.137
|
***
|
|
NA
|
shortest_path3:measurement_idD2
|
-0.216
|
0.080
|
-2.684
|
0.007
|
-0.374
|
-0.058
|
**
|
|
sub_id
|
sd__(Intercept)
|
0.705
|
NA
|
NA
|
NA
|
0.559
|
0.889
|
|
|
sub_id
|
sd__shortest_path2
|
0.437
|
NA
|
NA
|
NA
|
0.339
|
0.564
|
|
|
sub_id
|
sd__shortest_path3
|
0.220
|
NA
|
NA
|
NA
|
0.137
|
0.353
|
|
|
sub_id
|
sd__measurement_idD1
|
0.275
|
NA
|
NA
|
NA
|
0.193
|
0.391
|
|
|
sub_id
|
sd__measurement_idD2
|
0.285
|
NA
|
NA
|
NA
|
0.200
|
0.407
|
|
|
sub_id
|
cor__(Intercept).shortest_path2
|
-0.256
|
NA
|
NA
|
NA
|
-0.545
|
0.121
|
|
|
sub_id
|
cor__(Intercept).shortest_path3
|
0.487
|
NA
|
NA
|
NA
|
-0.449
|
0.629
|
|
|
sub_id
|
cor__(Intercept).measurement_idD1
|
-0.370
|
NA
|
NA
|
NA
|
-0.578
|
0.074
|
|
|
sub_id
|
cor__(Intercept).measurement_idD2
|
0.400
|
NA
|
NA
|
NA
|
-0.098
|
0.476
|
|
|
sub_id
|
cor__shortest_path2.shortest_path3
|
0.538
|
NA
|
NA
|
NA
|
0.141
|
0.799
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD1
|
0.103
|
NA
|
NA
|
NA
|
0.104
|
0.288
|
|
|
sub_id
|
cor__shortest_path2.measurement_idD2
|
0.044
|
NA
|
NA
|
NA
|
-0.147
|
0.342
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD1
|
-0.099
|
NA
|
NA
|
NA
|
-0.110
|
0.437
|
|
|
sub_id
|
cor__shortest_path3.measurement_idD2
|
0.376
|
NA
|
NA
|
NA
|
-0.487
|
0.641
|
|
|
sub_id
|
cor__measurement_idD1.measurement_idD2
|
0.356
|
NA
|
NA
|
NA
|
0.407
|
0.819
|
|
|
study
|
sd__(Intercept)
|
0.028
|
NA
|
NA
|
NA
|
0.004
|
0.181
|
|
predict_tr <- expand_grid(
measurement_id = c("D1", "D2", "D2b"),
shortest_path = factor(2:4),
sub_id = NA, study = NA
) %>%
predict_glmmTMB(stats_nav_tr_dist2)
plot_nav_tr <- nav_study3 %>%
filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
group_by(sub_id, measurement_id, shortest_path) %>%
summarise(accuracy = mean(correct), .groups = "drop") %>%
ggplot(aes(x=shortest_path, y=accuracy, color=measurement_id)) +
theme_custom() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_dotplot(
aes(fill = measurement_id),
binwidth = 0.01,
binaxis = "y", stackdir = "center",
position = position_dodge(width = 0.75),
dotsize = 1, alpha = 0.5, color = NA,
show.legend = FALSE
) +
geom_pointrange(
aes(
x = shortest_path, y = fit,
ymin = fit - se.fit, ymax = fit + se.fit,
color = measurement_id
),
data = predict_tr, inherit.aes = FALSE, show.legend = FALSE,
position = position_dodge(width = 0.25), linewidth = 1
) +
geom_line(
aes(
x = shortest_path, y = fit,
group = measurement_id, color = measurement_id
),
data = predict_tr, inherit.aes = FALSE,
position = position_dodge(width = 0.25), linewidth = 1
) +
scale_x_discrete(name = "Shortest path distance") +
scale_y_continuous(
name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
) +
scale_color_manual(
name = "Measurement",
values = c("D1"="#fa9fb5", "D2"="#7a0177", "D2b"="#238b45"),
labels = c(
"D1"="Before overnight rest",
"D2"="After overnight rest",
"D2b"="After reevaluation"
)
) +
scale_fill_manual(
values = c("D1"="#fa9fb5", "D2"="#7a0177", "D2b"="#238b45")
) +
coord_cartesian(ylim = c(0.3, 1.1)) +
theme(legend.position = "bottom") +
ggtitle("Navigation after transition reevaluation")
plot_nav_tr

if (knitting) {
ggsave(
here("figures", str_c("navigation_reevaluation", ".pdf")),
plot = plot_nav_tr,
width = 4, height = 4,
units = "in", dpi = 300
)
}
---
title: "Analyses for main text"
output:
  html_document:
    code_download: true
    code_folding: hide
    toc: true
    toc_float:
      collapsed: true
---

# Setup

```{r libraries}
library(tidyverse)
library(here)

library(glmmTMB)
library(broom.mixed)

library(tidygraph)
library(ggraph)

library(kableExtra)
library(tictoc)
```

```{r custom-functions}
source(here("code", "utils", "ggplot_themes.R"))
source(here("code", "utils", "modeling_utils.R"))
source(here("code", "utils", "representation_utils.R"))
source(here("code", "utils", "unicode_greek.R"))

predict_glmmTMB <- function(make_predictions_for, model_object) {
  make_predictions_for %>%
    bind_cols(
      predict(
        object = model_object,
        newdata = .,
        re.form = NA, allow.new.levels = TRUE, se.fit = TRUE, type = "response"
      )
    )
}

check_significance <- function(tidy_df) {
  tidy_df %>%
    mutate(
      sig = case_when(
        p.value < 0.001 ~ "***",
        p.value < 0.01 ~ "**",
        p.value < 0.05 ~ "*",
        p.value < 0.1 ~ ".",
        TRUE ~ ""
      )
    )
}
```

```{r knitting-setup}
# To control when to save figures
knitting <- knitr::is_html_output()

if (knitting) {
  if (!dir.exists(here("figures"))) {
    dir.create(here("figures"))
  }
}
```


# Network visualizations

```{r load-network-data}
adjlist <- here("data", "clean-data", "adjlist_learned.csv") %>%
  read_csv(show_col_types = FALSE)

g <- adjlist %>%
  filter(from < to, edge == 1) %>%
  select(-edge) %>%
  tbl_graph(edges = ., directed = FALSE)

adjlist_reevaluated <- here("data", "clean-data", "adjlist_reevaluated.csv") %>%
  read_csv(show_col_types = FALSE)

g_reevaluated <- adjlist_reevaluated %>%
  filter(from < to, edge == 1) %>%
  select(-edge) %>%
  tbl_graph(edges = ., directed = FALSE)
```

```{r plot-networks}
plot_network_learned <- g %>%
  mutate(name = row_number()) %>%
  ggraph("stress") +
  theme_network() +
  geom_edge_link() +
  geom_node_label(aes(label = name))

plot_network_reevaluated <- g_reevaluated %>%
  mutate(name = row_number()) %>%
  ggraph("stress") +
  theme_network() +
  geom_edge_link() +
  geom_node_label(aes(label = name))

plot_network_learned
plot_network_reevaluated

if (knitting) {
  ggsave(
    here("figures", str_c("network_learned", ".pdf")),
    plot = plot_network_learned,
    width = 4, height = 2,
    units = "in", dpi = 300
  )
  
  ggsave(
    here("figures", str_c("network_reevaluated", ".pdf")),
    plot = plot_network_reevaluated,
    width = 4, height = 2,
    units = "in", dpi = 300
  )
}
```


# Can humans solve social navigation problems?

In the paper, we start by examining social navigation behaviors in a one-day session (Study 1), or in the first session of a two-day study (Studies 2-3). Across all three studies, the procedure is exactly identical; studies 2-3 are, in this part of the dataset, exact replications of study 1.

```{r load-navigation-data}
nav_study1 <- here("data", "clean-data", "study1_message_passing.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == FALSE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(
    study = "Study 1",
    measurement_id = str_c("D", measurement_id),
    shortest_path = factor(shortest_path_given_opts)
  ) %>%
  select(
    study, sub_id, measurement_id, shortest_path,
    startpoint_id, endpoint_id,
    opt1_id, opt2_id,
    correct_choice, sub_choice,
    correct, rt
  )

nav_study2 <- here("data", "clean-data", "study2_message_passing.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == FALSE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(
    study = "Study 2",
    measurement_id = case_when(
      network == "learned" ~ str_c("D", measurement_id),
      network == "reevaluated" ~ "D2b"
    ),
    shortest_path = factor(shortest_path_given_opts)
  ) %>%
  select(
    study, sub_id, measurement_id, shortest_path,
    startpoint_id, endpoint_id,
    opt1_id, opt2_id,
    correct_choice, sub_choice,
    correct, rt
  )

nav_study3 <- here("data", "clean-data", "study3_message_passing.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == FALSE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(
    study = "Study 3",
    measurement_id = case_when(
      network == "reevaluated" ~ "D2b",
      measurement_id == 1 ~ "D1",
      measurement_id == 2 ~ "D1b",
      measurement_id == 3 ~ "D2"
    ),
    shortest_path = factor(shortest_path_given_opts)
  ) %>%
  select(
    study, sub_id, measurement_id, shortest_path,
    startpoint_id, endpoint_id,
    opt1_id, opt2_id,
    correct_choice, sub_choice,
    correct, rt
  )
```

We'll start off with some descriptive statistics of human behavior. To maximize statistical power, we will pool across studies whenever possible.

```{r descriptive-navigation-day1}
bind_rows(nav_study1, nav_study2, nav_study3) %>%
  filter(measurement_id == "D1") %>%
  group_by(measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  arrange(measurement_id, shortest_path) %>%
  pivot_wider(
    names_from = shortest_path, values_from = accuracy, names_prefix = "dist-"
  ) %>%
  kbl(
    caption = str_c(
      "<center>", "Descriptive: Navigation accuracy", "</center>"
    ),
    digits = 2
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

And now the inferential statistical tests. Note that we're interested in knowing whether navigation accuracy differs from chance at each distance, so we'll estimate the same statistical model multiple times, changing the reference category each time. Note that this only reparameterizes the model, such that the *same* variance is accounted for by different parameters; it does *not* change the total amount of variance accounted for.

```{r stats-day1}
nav_day1 <- bind_rows(nav_study1, nav_study2, nav_study3) %>%
  filter(measurement_id == "D1") %>%
  # Give every subject a distinct identifier
  mutate(sub_id = str_c(study, " s", sub_id))

stats_nav_day1_dist2 <- nav_day1 %>%
  mutate(shortest_path = fct_relevel(shortest_path, "2")) %>%
  glmmTMB(
    correct ~ shortest_path + (1 + shortest_path | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

stats_nav_day1_dist3 <- nav_day1 %>%
  mutate(shortest_path = fct_relevel(shortest_path, "3")) %>%
  glmmTMB(
    correct ~ shortest_path + (1 + shortest_path | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

stats_nav_day1_dist4 <- nav_day1 %>%
  mutate(shortest_path = fct_relevel(shortest_path, "4")) %>%
  glmmTMB(
    correct ~ shortest_path + (1 + shortest_path | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

map_dfr(
  .x = list(
    "dist-2" = stats_nav_day1_dist2,
    "dist-3" = stats_nav_day1_dist3,
    "dist-4" = stats_nav_day1_dist4
  ),
  .f = ~tidy(.x, conf.int = TRUE),
  .id = "ref_cat"
) %>%
  check_significance() %>%
  select(-c(ref_cat, effect, component)) %>%
  kbl(
    caption = str_c("<center>", "Navigation accuracy: Day 1", "</center>"),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive")) %>%
  pack_rows("Ref. Cat. dist-2", 1, 10) %>%
  pack_rows("Ref. Cat. dist-3", 11, 20) %>%
  pack_rows("Ref. Cat. dist-4", 21, 30)
```

We'll plot out the raw data, plus model predictions...

```{r plot-day1}
predict_nav_day1 <- expand_grid(
  measurement_id = "D1",
  shortest_path = factor(2:4),
  sub_id = NA, study = NA
) %>%
  predict_glmmTMB(stats_nav_day1_dist2)

plot_nav_day1 <- bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id == "D1") %>%
  group_by(sub_id, measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  ggplot(aes(x=shortest_path, y=accuracy)) +
  theme_custom() +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_dotplot(
    binwidth = 0.01,
    binaxis = "y", stackdir = "center",
    position = position_dodge(width = 0.75),
    dotsize = 1, alpha = 0.5, color = NA,
    show.legend = FALSE
  ) +
  geom_pointrange(
    aes(x = shortest_path, y = fit, ymin = fit - se.fit, ymax = fit + se.fit),
    data = predict_nav_day1, inherit.aes = FALSE, show.legend = FALSE,
    position = position_dodge(width = 0.15), linewidth = 1
  ) +
  geom_line(
    aes(x = shortest_path, y = fit, group = measurement_id),
    data = predict_nav_day1, inherit.aes = FALSE,
    position = position_dodge(width = 0.15), linewidth = 1
  ) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.1)
  ) +
  coord_cartesian(ylim = c(0.4, 1.1)) +
  theme(legend.position = "bottom") +
  ggtitle("Human social navigation")

plot_nav_day1

if (knitting) {
  ggsave(
    here("figures", str_c("navigation_day1", ".pdf")),
    plot = plot_nav_day1,
    width = 8/3, height = 3,
    units = "in", dpi = 300
  )
}
```


# Computational models of social navigation

We'd like to have some mechanistic insights about how people solve navigation problems. To do this, we'll look at two candidate models of navigation: breadth-first search (BFS) and the Successor Representation (SR).

## Simulations

### BFS simulation

```{r load-bfs-data}
sim_bfs <- here("data", "bfs-sims", "bfs_sims_learned.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == FALSE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(shortest_path = factor(shortest_path_given_opts)) %>%
  select(
    shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id,
    bfs_choice, bfs_correct_choice, bfs_n_visits_total
  )
```

In our implementation, we model the BFS agent having some "threshold" for searching through the network. This can be thought of as a "willingness to spend time/effort performing a search" threshold. Once that threshold is exceeded, it becomes increasingly likely that the agent gives up and chooses randomly.

To see what threshold values might be informative to look at, we'll look at the average number of "searches" that an agent must perform to make a (non-random) decision.

```{r avg-bfs-visits}
sim_bfs %>%
  group_by(shortest_path) %>%
  summarise(avg_n_searches = mean(bfs_n_visits_total)) %>%
  kbl(
    caption = str_c("<center>", "Average searches in online-BFS", "</center>"),
    digits = 2
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

And now we'll plot the model predictions...

```{r plot-bfs-sim}
bfs_avg_accuracy <- sim_bfs %>%
  group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
  summarise(
    bfs_accuracy = mean(bfs_correct_choice),
    bfs_visits = mean(bfs_n_visits_total),
    .groups = "drop"
  )

plot_sim_bfs <- bfs_avg_accuracy %>%
  expand_grid(search_threshold = seq(2, 12, 2)) %>%
  rowwise() %>%
  mutate(
    # Note: p(BFS) is 1-p(give up)
    p_bfs = softmax(
      option_values = c(search_threshold, bfs_visits),
      option_chosen = 1,
      temperature = 1
    )
  ) %>%
  ungroup() %>%
  # Weigh the model predictions according to their likelihood
  mutate(
    p_give_up = 1 - p_bfs,
    bfs_threshold_accuracy = (p_bfs * bfs_accuracy) + (p_give_up * (1/2))
  ) %>%
  # Format for plotting
  mutate(
    search_threshold = str_pad(search_threshold, width = 2, side = "left")
  ) %>%
  bind_rows(
    bfs_avg_accuracy %>%
      mutate(
        search_threshold = "Never gives up",
        bfs_threshold_accuracy = bfs_accuracy
      )
  ) %>%
  # Now plot
  ggplot(
    aes(
      x=shortest_path, y=bfs_threshold_accuracy,
      color=search_threshold, group=search_threshold
    )
  ) +
  theme_custom() +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  stat_summary(geom = "line", fun = mean, linewidth = 1) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(name = "Accuracy", labels = scales::percent) +
  scale_color_viridis_d(
    name = "Search threshold", option = "magma",
    begin = 0.1, end = 0.9, direction = -1
  ) +
  guides(color = guide_legend(byrow = TRUE, nrow = 1)) +
  coord_cartesian(ylim = c(0.5, 1)) +
  theme(
    panel.grid = element_blank(),
    legend.position = "bottom"
  ) +
  ggtitle("Simulated BFS navigation")

plot_sim_bfs

if (knitting) {
  ggsave(
    here("figures", str_c("simulated_bfs", ".pdf")),
    plot = plot_sim_bfs,
    width = 8/3, height = 3,
    units = "in", dpi = 300
  )
}
```

### SR navigation simulation

```{r get-triallist}
triallist_nav_learned <- nav_study1 %>%
  filter(sub_id == 1) %>%
  select(
    startpoint_id, endpoint_id, opt1_id, opt2_id,
    correct_choice, shortest_path
  ) %>%
  arrange(startpoint_id, endpoint_id)
```

We'll create some simulated learning observations.

```{r simulate-observations}
set.seed(sum(utf8ToInt("Jenny and me was like peas and carrots")))

simulated_paired_associates <- adjlist %>%
  filter(edge == 1) %>%
  select(from, to) %>%
  expand_grid(set = 1:5000, .) %>%
  group_by(set) %>%
  slice_sample(prop = 1) %>%
  ungroup()
```

And now we'll simulate an "asymptotic" SR and how it performs in the navigation task.

```{r sim-sr-asymptotic}
simulate_sr <- function(simulated_observations) {
  simulated_observations %>%
    expand_grid(gamma = seq(0.1, 0.9, 0.1)) %>%
    group_by(gamma) %>%
    nest() %>%
    mutate(
      sr = map(
        .x = data,
        .f = ~build_rep_sr(
          learning_data = .x, this_alpha = 0.1, this_gamma = gamma
        )
      )
    ) %>%
    unnest(sr) %>%
    ungroup() %>%
    select(-data)
}

join_sr <- function(navigation_triallist, simulated_sr) {
  navigation_triallist %>%
    left_join(
      simulated_sr %>%
        rename(opt1_sr = sr_value, opt1_id = from, endpoint_id = to)
    ) %>%
    left_join(
      simulated_sr %>%
        rename(opt2_sr = sr_value, opt2_id = from, endpoint_id = to)
    )
}

sim_sr_matrix_asymptotic <- simulated_paired_associates %>%
  simulate_sr()

sim_sr_behavior_asymptotic <- join_sr(
  triallist_nav_learned, sim_sr_matrix_asymptotic
) %>%
  # Feed values through softmax
  mutate(across(c(opt1_sr, opt2_sr), ~.x * 100)) %>%
  expand_grid(temperature = 1) %>%
  rowwise() %>%
  mutate(
    p_correct = softmax(
      option_values = c(opt1_sr, opt2_sr),
      option_chosen = if_else(correct_choice == opt1_id, 1, 2),
      temperature = temperature,
      use_inverse_temperature = TRUE
    )
  ) %>%
  ungroup()
```

```{r plot-sr-sim-asymptotic}
plot_sim_sr_asymptotic <- sim_sr_behavior_asymptotic %>%
  mutate(
    gamma = factor(gamma),
    temperature = str_pad(temperature, width = 2, side = "left"),
    temperature = str_c(unicode_greek["tau"], " = ", temperature)
  ) %>%
  group_by(gamma, temperature, shortest_path) %>%
  summarise(p_correct = mean(p_correct), .groups = "drop") %>%
  ggplot(aes(x=shortest_path, y=p_correct, color=gamma)) +
  theme_custom() +
  facet_grid(rows = vars(temperature)) +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_line(aes(color = gamma, group = gamma), linewidth = 0.8) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(name = "Accuracy", labels = scales::percent) +
  scale_color_viridis_d(
    name = str_c(unicode_greek["gamma"], " = "), option = "turbo", end = 0.9
  ) +
  guides(color = guide_legend(byrow = TRUE, nrow = 1)) +
  coord_cartesian(ylim = c(0.5, 1)) +
  theme(legend.position = "bottom") +
  ggtitle("Simulated SR navigation")

plot_sim_sr_asymptotic

if (knitting) {
  suppressWarnings(
    ggsave(
      here("figures", str_c("simulated_sr", ".pdf")),
      plot = plot_sim_sr_asymptotic,
      width = 8/3, height = 3,
      units = "in", dpi = 300,
    )
  )
  
  ggsave(
    here("figures", str_c("simulated_sr_cairo", ".pdf")),
    plot = plot_sim_sr_asymptotic +
      guides(color = guide_legend(byrow = TRUE, nrow = 2)),
    width = 8/3, height = 3,
    units = "in", dpi = 300,
    device = cairo_pdf
  )
}
```

## Model comparison

To do model comparison, we'll need to pull in the estimated parameters/likelihoods from the model-fitting.

```{r load-params}
clean_params_from_raw <- FALSE

if (clean_params_from_raw) {
  params <- here("data", "param-fits") %>%
    fs::dir_ls(regexp = "study[[:digit:]]_D[[:digit:]]b?") %>%
    map_dfr(
      .f = ~read_csv(.x, show_col_types = FALSE) %>%
        best_optim_run("dataframe"),
      .id = "filename"
    ) %>%
    mutate(
      study = str_extract(filename, "study[[:digit:]]"),
      study = str_remove(study, "study"),
      study = str_c("Study ", study),
      measurement_id = str_extract(filename, "_D[[:digit:]]b?"),
      measurement_id = str_remove(measurement_id, "_"),
      sub_id = str_extract(filename, "sub-[[:digit:]]+"),
      sub_id = str_remove(sub_id, "sub-"),
      sub_id = as.integer(sub_id),
      model = case_when(
        str_detect(filename, "_hybrid-") ~ "hybrid",
        str_detect(filename, "_sr-") ~ "sr",
        str_detect(filename, "_bfs-") ~ "bfs"
      )
    ) %>%
    select(
      study, sub_id, measurement_id,
      model,
      param_name, param_value = param_value_human_readable,
      neg_loglik = optim_value
    ) %>%
    arrange(study, sub_id, measurement_id, model)
  
  params %>%
    write_csv(here("data", "param-fits", "clean_param_fits.csv"))
} else {
  params <- here("data", "param-fits", "clean_param_fits.csv") %>%
    read_csv(show_col_types = FALSE)
}
```

```{r pxp-model-selection}
source(here("code", "utils", "bayesian_model_selection.R"))

pxp_results <- params %>%
  select(study, sub_id, measurement_id, model, neg_loglik) %>%
  distinct() %>%
  mutate(log_lik = -neg_loglik) %>%
  select(-neg_loglik) %>%
  pivot_wider(names_from = model, values_from = log_lik) %>%
  select(-sub_id) %>%
  group_by(study, measurement_id) %>%
  nest() %>%
  mutate(
    test = map(
      .x = data,
      .f = ~bayesian_model_selection(.x)
    )
  ) %>%
  unnest(test) %>%
  ungroup() %>%
  select(-data)

pxp_results %>%
  ggplot(aes(x=measurement_id, y=pxp, color=model)) +
  theme_custom() +
  facet_wrap(~study, scales = "free_x") +
  geom_point(
    position = position_dodge(width = 0.75)
  )

pxp_results %>%
  select(study, measurement_id, model, pxp) %>%
  mutate(pxp = as.numeric(pxp)) %>%
  pivot_wider(
    names_from = model,
    values_from = pxp,
    names_prefix = "pxp_"
  ) %>%
  kbl(
    caption = str_c("<center>", "PXP results", "</center>"),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

## Posterior predictive check

We'll start by simulating the model's predictions for each subject, given their estimated parameters.

```{r ppc-bfs}
ppc_bfs <- bind_rows(nav_study1, nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2")) %>%
  left_join(
    params %>%
      filter(model == "bfs") %>%
      pivot_wider(names_from = param_name, values_from = param_value) %>%
      select(study, sub_id, measurement_id, search_threshold, lapse_rate)
  ) %>%
  left_join(bfs_avg_accuracy) %>%
  mutate(
    p_sub_choice_bfs = if_else(
      sub_choice == correct_choice,
      bfs_accuracy,
      1 - bfs_accuracy
    )
  ) %>%
  # What's the probability of *completing* BFS-online all the way through?
  rowwise() %>%
  mutate(
    search_threshold = search_threshold,
    p_complete_bfs = softmax(
      option_values = c(search_threshold, bfs_visits),
      option_chosen = 1,
      temperature = 1
    )
  ) %>%
  ungroup() %>%
  # Weigh BFS predictions accordingly
  mutate(
    p_give_up = 1 - p_complete_bfs,
    predicted_correct = (p_complete_bfs * p_sub_choice_bfs) + (p_give_up * 1/2),
    ### Add lapse rate
    #   Dividing by 2 is because there are two options to choose from
    #   Therefore, when lapse rate = 1, this becomes chance = 1/2
    predicted_correct = predicted_correct * (1-lapse_rate) + (lapse_rate/2)
  ) %>%
  # Average over trials
  group_by(study, sub_id, measurement_id, shortest_path) %>%
  summarise(
    empirical = mean(correct),
    predicted = mean(predicted_correct),
    .groups = "drop"
  )
```

```{r ppc-sr}
ppc_sr_matrix <- params %>%
  filter(model == "sr") %>%
  select(
    study, sub_id, measurement_id, name = param_name, value = param_value
  ) %>%
  pivot_wider() %>%
  group_by(study, sub_id, measurement_id) %>%
  nest() %>%
  mutate(
    sim_sr = map(
      .x = data,
      .f = ~build_rep_sr(
        learning_data = simulated_paired_associates %>% filter(set %in% 1:100),
        this_alpha = 0.1,
        this_gamma = .x$sr_gamma,
        bidirectional = TRUE
      )
    )
  ) %>%
  unnest(sim_sr) %>%
  unnest(data) %>%
  ungroup()

ppc_sr_behavior <- bind_rows(nav_study1, nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2")) %>%
  left_join(
    ppc_sr_matrix %>%
      rename(opt1_id = from, endpoint_id = to, opt1_sr = sr_value)
  ) %>%
  left_join(
    ppc_sr_matrix %>%
      rename(opt2_id = from, endpoint_id = to, opt2_sr = sr_value)
  ) %>%
  rowwise() %>%
  mutate(
    predicted_correct = softmax(
      option_values = c(opt1_sr, opt2_sr) * 100,
      option_chosen = if_else(correct_choice == opt1_id, 1, 2),
      temperature = softmax_temperature,
      use_inverse_temperature = TRUE,
      lapse_rate = lapse_rate
    )
  ) %>%
  ungroup() %>%
  # Fix trials when the softmax becomes undefined
  mutate(
    predicted_correct = case_when(
      is.nan(predicted_correct) & (sub_choice == correct_choice) ~ 1,
      is.nan(predicted_correct) & (sub_choice != correct_choice) ~ 0,
      TRUE ~ predicted_correct
    )
  ) %>%
  # Average over trials
  group_by(study, sub_id, measurement_id, shortest_path) %>%
  summarise(
    empirical = mean(correct),
    predicted = mean(predicted_correct),
    .groups = "drop"
  )
```

Now that we have both PPCs, we can plot them side-by-side.

```{r plot-ppc-day1}
plot_ppc_day1 <- ppc_bfs %>%
  rename(predicted_bfs = predicted) %>%
  left_join(ppc_sr_behavior %>% rename(predicted_sr = predicted)) %>%
  pivot_longer(
    c(empirical, predicted_bfs, predicted_sr),
    names_to = "agent", values_to = "accuracy"
  ) %>%
  filter(measurement_id == "D1") %>%
  ggplot(aes(x=shortest_path, y=accuracy)) +
  theme_custom() +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_point(
    aes(color = agent),
    alpha = 0.05,
    position = position_jitterdodge(
      jitter.width = 0.2, jitter.height = 0, dodge.width = 0.75, seed = 1
    ),
    show.legend = FALSE
  ) +
  stat_summary(
    aes(color = agent), geom = "crossbar", fun = mean,
    position = position_dodge(0.5)
  ) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
  ) +
  scale_color_manual(
    name = NULL,
    values = c(
      "empirical"="#fd8d3c",
      "predicted_bfs"="#8c2d04",
      "predicted_sr"="#bd0026"
    ),
    labels = c("empirical"="Human", "predicted_bfs"="BFS", "predicted_sr"="SR")
  ) +
  theme(legend.position = "bottom") +
  ggtitle("Posterior predictive check")

plot_ppc_day1

if (knitting) {
  ggsave(
    here("figures", str_c("ppc_day1", ".pdf")),
    plot = plot_ppc_day1,
    width = 8/2, height = 2.5,
    units = "in", dpi = 300
  )
}
```

## Held-out trials

There were some trials where both Sources had equivalent shortest path distances from the Target. We would expect that BFS would be largely indifferent between the two Sources, but it is possible that humans and/or the SR would make other predictions.

```{r load-tie-data}
nav_study1_ties <- here("data", "clean-data", "study1_message_passing.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == TRUE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(
    study = "Study 1",
    measurement_id = str_c("D", measurement_id),
    shortest_path = factor(shortest_path_given_opts)
  ) %>%
  select(
    study, sub_id, measurement_id, shortest_path,
    startpoint_id, endpoint_id,
    opt1_id, opt2_id,
    correct_choice, sub_choice,
    correct, rt
  ) %>%
  filter(measurement_id %in% c("D1", "D2"))

nav_study2_ties <- here("data", "clean-data", "study2_message_passing.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == TRUE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(
    study = "Study 2",
    measurement_id = case_when(
      network == "learned" ~ str_c("D", measurement_id),
      network == "reevaluated" ~ "D2b"
    ),
    shortest_path = factor(shortest_path_given_opts)
  ) %>%
  select(
    study, sub_id, measurement_id, shortest_path,
    startpoint_id, endpoint_id,
    opt1_id, opt2_id,
    correct_choice, sub_choice,
    correct, rt
  ) %>%
  filter(measurement_id %in% c("D1", "D2"))

nav_study3_ties <- here("data", "clean-data", "study3_message_passing.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == TRUE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(
    study = "Study 3",
    measurement_id = case_when(
      network == "reevaluated" ~ "D2b",
      measurement_id == 1 ~ "D1",
      measurement_id == 2 ~ "D1b",
      measurement_id == 3 ~ "D2"
    ),
    shortest_path = factor(shortest_path_given_opts)
  ) %>%
  select(
    study, sub_id, measurement_id, shortest_path,
    startpoint_id, endpoint_id,
    opt1_id, opt2_id,
    correct_choice, sub_choice,
    correct, rt
  ) %>%
  filter(measurement_id %in% c("D1", "D2"))
```

```{r ties-human}
tie_item_analysis_humans <- bind_rows(
  nav_study1_ties, nav_study2_ties, nav_study3_ties
) %>%
  filter(measurement_id == "D1") %>%
  group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
  summarise(
    p_human_opt1 = mean(sub_choice == opt1_id),
    .groups = "drop"
  )
```

```{r ties-bfs}
bfs_avg_accuracy_ties <- here("data", "bfs-sims", "bfs_sims_learned.csv") %>%
  read_csv(show_col_types = FALSE) %>%
  filter(
    two_correct_options == TRUE,
    shortest_path_given_opts == shortest_path_given_start_end
  ) %>%
  mutate(shortest_path = factor(shortest_path_given_opts)) %>%
  select(
    shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id,
    bfs_choice, bfs_correct_choice, bfs_n_visits_total
  ) %>%
  group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
  summarise(
    p_bfs_opt1 = mean(bfs_choice == opt1_id),
    bfs_visits = mean(bfs_n_visits_total),
    .groups = "drop"
  )

tie_item_analysis_bfs <- bind_rows(
  nav_study1_ties, nav_study2_ties, nav_study3_ties
) %>%
  filter(measurement_id == "D1") %>%
  left_join(
    params %>%
      filter(model == "bfs") %>%
      pivot_wider(names_from = param_name, values_from = param_value) %>%
      select(study, sub_id, measurement_id, search_threshold, lapse_rate)
  ) %>%
  left_join(bfs_avg_accuracy_ties) %>%
  mutate(
    p_sub_choice_bfs = if_else(
      sub_choice == opt1_id,
      p_bfs_opt1,
      1 - p_bfs_opt1
    )
  ) %>%
  select(-p_bfs_opt1) %>%
  # What's the probability of *completing* BFS-online all the way through?
  rowwise() %>%
  mutate(
    search_threshold = search_threshold,
    p_complete_bfs = softmax(
      option_values = c(search_threshold, bfs_visits),
      option_chosen = 1,
      temperature = 1
    )
  ) %>%
  ungroup() %>%
  # Weigh BFS predictions accordingly
  mutate(
    p_give_up = 1 - p_complete_bfs,
    p_bfs_opt1 = (p_complete_bfs * p_sub_choice_bfs) + (p_give_up * 1/2),
    ### Add lapse rate
    #   Dividing by 2 is because there are two options to choose from
    #   Therefore, when lapse rate = 1, this becomes chance = 1/2
    p_bfs_opt1 = p_bfs_opt1 * (1-lapse_rate) + (lapse_rate/2)
  ) %>%
  # Average over trials
  # group_by(shortest_path) %>%
  group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
  summarise(
    p_bfs_opt1 = mean(p_bfs_opt1),
    .groups = "drop"
  )
```

```{r ties-sr}
tie_item_analysis_sr <- bind_rows(
  nav_study1_ties, nav_study2_ties, nav_study3_ties
) %>%
  filter(measurement_id == "D1") %>%
  left_join(
    ppc_sr_matrix %>%
      select(
        study, sub_id, measurement_id,
        opt1_id = from,
        endpoint_id = to,
        opt1_sr = sr_value
      )
  ) %>%
  left_join(
    ppc_sr_matrix %>%
      select(
        study, sub_id, measurement_id,
        opt2_id = from,
        endpoint_id = to,
        opt2_sr = sr_value,
        sr_gamma, softmax_temperature, lapse_rate
      )
  ) %>%
  rowwise() %>%
  mutate(
    p_sr_opt1 = softmax(
      option_values = c(opt1_sr, opt2_sr) * 100,
      option_chosen = 1,
      temperature = softmax_temperature,
      use_inverse_temperature = TRUE,
      lapse_rate = lapse_rate
    )
  ) %>%
  group_by(shortest_path, startpoint_id, endpoint_id, opt1_id, opt2_id) %>%
  summarise(
    p_sr_opt1 = mean(p_sr_opt1, na.rm = TRUE),
    .groups = "drop"
  )
```

```{r plot-ties}
plot_ties <- tie_item_analysis_humans %>%
  left_join(tie_item_analysis_bfs) %>%
  left_join(tie_item_analysis_sr) %>%
  mutate(item = row_number(), item = factor(item)) %>%
  pivot_longer(
    starts_with("p_"),
    names_to = "agent",
    values_to = "p_choose_opt1"
  ) %>%
  mutate(
    agent = str_remove_all(agent, "p_|_opt1"),
    agent = case_when(
      agent == "human" ~ "Human",
      agent == "sr" ~ "SR",
      agent == "bfs" ~ "BFS",
    ),
    agent = fct_relevel(agent, "Human", "SR")
  ) %>%
  ggplot(aes(x=shortest_path, y=p_choose_opt1)) +
  theme_custom() +
  facet_wrap(~agent) +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_point(alpha = 0.25) +
  stat_summary(geom = "crossbar", fun = mean, color = "red") +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "p(Choose Source A > B)",
    labels = scales::percent, breaks = seq(0, 1, 0.25)
  ) +
  theme(
    panel.grid = element_blank(),
    legend.position = "bottom"
  ) +
  ggtitle("Navigation problems with two correct answers")

plot_ties

if (knitting) {
  ggsave(
    here("figures", str_c("navigation_heldout_day1", ".pdf")),
    plot = plot_ties,
    width = 8/2, height = 2.5,
    units = "in", dpi = 300
  )
}
```


# Does navigation improve after rest?

First, some descriptives...

```{r descriptive-navigation-all}
bind_rows(nav_study1, nav_study2, nav_study3) %>%
  group_by(measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  arrange(measurement_id, shortest_path) %>%
  pivot_wider(
    names_from = shortest_path, values_from = accuracy, names_prefix = "dist-"
  ) %>%
  kbl(
    caption = str_c(
      "<center>", "Descriptive: Navigation accuracy", "</center>"
    ),
    digits = 2
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

## Overnight rest

Now we want to see how navigation accuracy changes from day 1 to day 2. This pools across studies 2-3 (study 1 was only a one-day experiment).

```{r stats-day1-to-day2}
nav_day1_to_day2 <- bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2")) %>%
  # Give every subject a distinct identifier
  mutate(sub_id = str_c(study, " s", sub_id))

stats_nav_day2_dist2 <- nav_day1_to_day2 %>%
  mutate(shortest_path = fct_relevel(shortest_path, "2")) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

stats_nav_day2_dist3 <- nav_day1_to_day2 %>%
  mutate(shortest_path = fct_relevel(shortest_path, "3")) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

stats_nav_day2_dist4 <- nav_day1_to_day2 %>%
  mutate(shortest_path = fct_relevel(shortest_path, "4")) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

map_dfr(
  .x = list(
    "dist-2" = stats_nav_day2_dist2,
    "dist-3" = stats_nav_day2_dist3,
    "dist-4" = stats_nav_day2_dist4
  ),
  .f = ~tidy(.x, conf.int = TRUE),
  .id = "ref_cat"
) %>%
  check_significance() %>%
  select(-c(ref_cat, effect, component)) %>%
  kbl(
    caption = str_c(
      "<center>", "Navigation accuracy: Day 1 to Day 2", "</center>"
    ),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive")) %>%
  pack_rows("Ref. Cat. dist-2", 1, 17) %>%
  pack_rows("Ref. Cat. dist-3", 18, 34) %>%
  pack_rows("Ref. Cat. dist-4", 35, 51)
```

```{r plot-day1-to-day2}
predict_nav_day1_to_day2 <- expand_grid(
  measurement_id = c("D1", "D2"),
  shortest_path = factor(2:4),
  sub_id = NA, study = NA
) %>%
  predict_glmmTMB(stats_nav_day2_dist2)

plot_nav_day1_to_day2 <- nav_day1_to_day2 %>%
  group_by(sub_id, measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  ggplot(aes(x=shortest_path, y=accuracy, color=measurement_id)) +
  theme_custom() +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_dotplot(
    aes(fill = measurement_id),
    binwidth = 0.01,
    binaxis = "y", stackdir = "center",
    position = position_dodge(width = 0.75),
    dotsize = 1, alpha = 0.5, color = NA,
    show.legend = FALSE
  ) +
  geom_pointrange(
    aes(
      x = shortest_path, y = fit,
      ymin = fit - se.fit, ymax = fit + se.fit,
      color = measurement_id
    ),
    data = predict_nav_day1_to_day2, inherit.aes = FALSE, show.legend = FALSE,
    position = position_dodge(width = 0.15), linewidth = 1
  ) +
  geom_line(
    aes(
      x = shortest_path, y = fit,
      group = measurement_id, color = measurement_id
    ),
    data = predict_nav_day1_to_day2, inherit.aes = FALSE,
    position = position_dodge(width = 0.15), linewidth = 1
  ) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
  ) +
  scale_color_manual(
    name = "Measurement",
    values = c("D1"="#fa9fb5", "D2"="#7a0177"),
    labels = c("D1"="Before overnight rest", "D2"="After overnight rest")
  ) +
  scale_fill_manual(values = c("D1"="#fa9fb5", "D2"="#7a0177")) +
  coord_cartesian(ylim = c(0.3, 1.1)) +
  theme(legend.position = "bottom") +
  ggtitle("Navigation after overnight rest")

plot_nav_day1_to_day2

if (knitting) {
  ggsave(
    here("figures", str_c("navigation_day1_to_day2", ".pdf")),
    plot = plot_nav_day1_to_day2,
    width = 4, height = 3,
    units = "in", dpi = 300
  )
}
```

## Awake rest

Is a brief period of awake rest sufficient for improving navigation accuracy?

```{r stats-awake-rest}
stats_nav_awake_dist2 <- nav_study3 %>%
  filter(measurement_id %in% c("D1", "D1b")) %>%
  mutate(shortest_path = fct_relevel(shortest_path, "2")) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id),
    family = binomial,
    data = .
  )

stats_nav_awake_dist3 <- nav_study3 %>%
  filter(measurement_id %in% c("D1", "D1b")) %>%
  mutate(shortest_path = fct_relevel(shortest_path, "3")) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id),
    family = binomial,
    data = .
  )

stats_nav_awake_dist4 <- nav_study3 %>%
  filter(measurement_id %in% c("D1", "D1b")) %>%
  mutate(shortest_path = fct_relevel(shortest_path, "4")) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id),
    family = binomial,
    data = .
  )

map_dfr(
  .x = list(
    "dist-2" = stats_nav_awake_dist2,
    "dist-3" = stats_nav_awake_dist3,
    "dist-4" = stats_nav_awake_dist4
  ),
  .f = ~tidy(.x, conf.int = TRUE),
  .id = "ref_cat"
) %>%
  check_significance() %>%
  select(-c(ref_cat, effect, component)) %>%
  kbl(
    caption = str_c("<center>", "Navigation accuracy: Awake Rest", "</center>"),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive")) %>%
  pack_rows("Ref. Cat. dist-2", 1, 16) %>%
  pack_rows("Ref. Cat. dist-3", 17, 32) %>%
  pack_rows("Ref. Cat. dist-4", 33, 48)
```

```{r plot-awake-rest}
predict_nav_awake <- expand_grid(
  measurement_id = c("D1", "D1b"),
  shortest_path = factor(2:4),
  sub_id = NA, study = NA
) %>%
  predict_glmmTMB(stats_nav_awake_dist2)

plot_nav_awake <- nav_study3 %>%
  filter(measurement_id %in% c("D1", "D1b")) %>%
  group_by(sub_id, measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  ggplot(aes(x=shortest_path, y=accuracy, color=measurement_id)) +
  theme_custom() +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_dotplot(
    aes(fill = measurement_id),
    binwidth = 0.01,
    binaxis = "y", stackdir = "center",
    position = position_dodge(width = 0.75),
    dotsize = 1, alpha = 0.5, color = NA,
    show.legend = FALSE
  ) +
  geom_pointrange(
    aes(
      x = shortest_path, y = fit,
      ymin = fit - se.fit, ymax = fit + se.fit,
      color = measurement_id
    ),
    data = predict_nav_awake, inherit.aes = FALSE, show.legend = FALSE,
    position = position_dodge(width = 0.15), linewidth = 1
  ) +
  geom_line(
    aes(
      x = shortest_path, y = fit,
      group = measurement_id, color = measurement_id
    ),
    data = predict_nav_awake, inherit.aes = FALSE,
    position = position_dodge(width = 0.15), linewidth = 1
  ) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
  ) +
  scale_color_manual(
    name = "Measurement",
    values = c("D1"="#fa9fb5", "D1b"="#2c7fb8"),
    labels = c("D1"="Before overnight rest", "D1b"="After awake rest")
  ) +
  scale_fill_manual(values = c("D1"="#fa9fb5", "D1b"="#2c7fb8")) +
  coord_cartesian(ylim = c(0.3, 1.1)) +
  theme(legend.position = "bottom") +
  ggtitle("Navigation after awake rest")

plot_nav_awake

if (knitting) {
  ggsave(
    here("figures", str_c("navigation_awake_rest", ".pdf")),
    plot = plot_nav_awake,
    width = 4, height = 3,
    units = "in", dpi = 300
  )
}
```

## SR replay simulation

Before starting to do any simulation, we first want to know how much replay an agent can fit into different periods of time. Based on past research measuring neural replay events, we'll assume that it takes about 50ms for the brain to replay a single item from a sequence. This will let us calculate the total number of items that could be replayed in each epoch. To simplify the process of actually running the simulation, we'll convert this quantity into the number of "sets" that can be replayed, where a single "set" consists of the total number of relationships in the network (i.e., the number of undirected edges = 17, meaning that each set consists of 34 observations because each set contains observations of both A->B and B->A).

```{r avail-sr-replay-time}
tibble(minutes_for_replay = c(1, 5, 15, 30, 60, 120)) %>%
  mutate(
    n_items = minutes_for_replay / (0.05 / 60),
    n_sets = n_items / 34
  ) %>%
  kbl(
    caption = str_c("<center>", "SR replay time", "</center>"),
    digits = 2
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

```{r simulate-sr-replay}
sim_replay_0_min <- simulated_paired_associates %>%
  filter(set <= 6) %>%
  simulate_sr() %>%
  mutate(replay_time = 0)

sim_replay_1_min <- simulated_paired_associates %>%
  filter(set <= 35 + 6) %>%
  simulate_sr() %>%
  mutate(replay_time = 1)

sim_replay_5_min <- simulated_paired_associates %>%
  filter(set <= 176 + 6) %>%
  simulate_sr() %>%
  mutate(replay_time = 5)

sim_replay_60_min <- simulated_paired_associates %>%
  filter(set <= 2117 + 6) %>%
  simulate_sr() %>%
  mutate(replay_time = 60)
```

```{r plot-sr-sim-replay}
plot_sim_sr_replay <- bind_rows(
  join_sr(triallist_nav_learned, sim_replay_0_min),
  join_sr(triallist_nav_learned, sim_replay_1_min),
  join_sr(triallist_nav_learned, sim_replay_5_min),
  join_sr(triallist_nav_learned, sim_replay_60_min)
) %>%
  # Feed values through softmax
  mutate(across(c(opt1_sr, opt2_sr), ~.x * 100)) %>%
  expand_grid(temperature = 1) %>%
  rowwise() %>%
  mutate(
    p_correct = softmax(
      option_values = c(opt1_sr, opt2_sr),
      option_chosen = if_else(correct_choice == opt1_id, 1, 2),
      temperature = temperature,
      use_inverse_temperature = TRUE
    )
  ) %>%
  ungroup() %>%
  # For plotting
  mutate(
    gamma = factor(gamma),
    temperature = str_pad(temperature, width = 2, side = "left"),
    temperature = str_c(unicode_greek["tau"], " = ", temperature),
    replay_time = str_pad(replay_time, width = 2, side = "left"),
    replay_time = case_when(
      str_detect(replay_time, "1$") ~ str_c(replay_time, " minute of replay"),
      replay_time == " 0" ~ "No replay",
      TRUE ~ str_c(replay_time, " minutes of replay")
    ),
    replay_time = fct_relevel(replay_time, "No replay")
  ) %>%
  group_by(replay_time, gamma, temperature, shortest_path) %>%
  summarise(p_correct = mean(p_correct), .groups = "drop") %>%
  # Plot
  ggplot(aes(x=shortest_path, y=p_correct, color=gamma)) +
  theme_custom() +
  facet_grid(cols = vars(replay_time), rows = vars(temperature)) +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_line(aes(color = gamma, group = gamma), linewidth = 0.8) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(name = "Accuracy", labels = scales::percent) +
  scale_color_viridis_d(
    name = str_c(unicode_greek["gamma"], " = "), option = "turbo", end = 0.9
  ) +
  guides(color = guide_legend(byrow = TRUE, nrow = 1)) +
  coord_cartesian(ylim = c(0.5, 1)) +
  theme(legend.position = "bottom") +
  ggtitle("Simulated effects of SR replay on navigation")

plot_sim_sr_replay

if (knitting) {
  ggsave(
    here("figures", str_c("simulated_replay", ".pdf")),
    plot = plot_sim_sr_replay,
    width = 7, height = 2.5,
    units = "in", dpi = 300,
    device = cairo_pdf
  )
}
```

## PPC before/after rest

```{r plot-ppc-day1-day2}
plot_ppc_day1_to_day2 <- ppc_sr_behavior %>%
  filter(study %in% c("Study 2", "Study 3")) %>%
  mutate(
    measurement_id = case_when(
      measurement_id == "D1" ~ "Before rest",
      measurement_id == "D2" ~ "After rest"
    ),
    measurement_id = fct_relevel(measurement_id, "Before rest")
  ) %>%
  pivot_longer(c(empirical, predicted)) %>%
  ggplot(aes(x=shortest_path, y=value)) +
  theme_custom() +
  facet_wrap(~measurement_id) +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_point(
    aes(color = name),
    alpha = 0.05,
    position = position_jitterdodge(
      jitter.width = 0.2, jitter.height = 0, dodge.width = 0.5, seed = 1
    ),
    show.legend = FALSE
  ) +
  stat_summary(
    aes(color = name), geom = "crossbar", fun = mean,
    position = position_dodge(0.5)
  ) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
  ) +
  scale_color_manual(
    name = NULL,
    values = c("empirical"="#fd8d3c", "predicted"="#bd0026"),
    labels = c("empirical"="Human", "predicted"="SR")
  ) +
  theme(legend.position = "bottom") +
  ggtitle("Posterior predictive check")

plot_ppc_day1_to_day2

if (knitting) {
  ggsave(
    here("figures", str_c("ppc_day2", ".pdf")),
    plot = plot_ppc_day1_to_day2,
    width = 8/3, height = 2.5,
    units = "in", dpi = 300
  )
}
```


## Relating model parameters to navigation

Does estimated gamma significantly increase after overnight rest?

```{r stats-gamma-increase}
params %>%
  filter(
    study %in% c("Study 2", "Study 3"),
    measurement_id %in% c("D1", "D2"),
    model == "sr",
    param_name == "sr_gamma"
  ) %>%
  select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
  group_by(measurement_id) %>%
  summarise(median_gamma = median(sr_gamma)) %>%
  kbl(
    caption = str_c(
      "<center>", "Median SR gamma before/after overnight rest", "</center>"
    ),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))

params %>%
  filter(
    study %in% c("Study 2", "Study 3"),
    measurement_id %in% c("D1", "D2"),
    model == "sr",
    param_name == "sr_gamma"
  ) %>%
  select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
  pivot_wider(names_from = measurement_id, values_from = sr_gamma) %>%
  with(
    wilcox.test(
      D2, D1, alternative = "greater", paired = TRUE, conf.int = TRUE
    )
  ) %>%
  tidy() %>%
  kbl(
    caption = str_c(
      "<center>", "Increase in SR gamma after overnight rest", "</center>"
    ),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

```{r plot-gamma-increase}
plot_gamma_change <- params %>%
  filter(
    study %in% c("Study 2", "Study 3"),
    measurement_id %in% c("D1", "D2"),
    model == "sr",
    param_name == "sr_gamma"
  ) %>%
  select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
  ggplot(aes(x=measurement_id, y=sr_gamma, color=measurement_id)) +
  theme_custom() +
  geom_point(
    position = position_jitterdodge(
      jitter.width = 0.25, jitter.height = 0, dodge.width = 0.5, seed = 1
    ),
    alpha = 0.25,
    show.legend = FALSE
  ) +
  stat_summary(
    geom = "crossbar", fun = median, position = "dodge", show.legend = FALSE
  ) +
  scale_x_discrete(
    name = "Measurement",
    labels = c("D1"="Before rest", "D2"="After rest")
  ) +
  scale_y_continuous(
    name = str_c("Estimated ", unicode_greek["gamma"]),
    breaks = seq(0, 1, 0.25)
  ) +
  scale_color_manual(
    name = "Measurement",
    values = c("D1"="#fa9fb5", "D2"="#7a0177"),
    labels = c("D1"="Before rest", "D2"="After rest")
  ) +
  coord_cartesian(ylim = c(0, 1.1)) +
  theme(legend.position = "bottom") +
  ggtitle(
    str_c(
      unicode_greek["Delta"], unicode_greek["gamma"], " after overnight rest"
    )
  )

plot_gamma_change

if (knitting) {
  ggsave(
    here("figures", str_c("gamma_change", ".pdf")),
    plot = plot_gamma_change,
    width = 8/3, height = 2.5,
    units = "in", dpi = 300,
    device = cairo_pdf
  )
}
```

Are changes in estimated gamma related to changes in navigation behaviors?

```{r stats-gamma-accuracy}
bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2")) %>%
  group_by(study, sub_id, measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  pivot_wider(names_from = measurement_id, values_from = accuracy) %>%
  mutate(delta_accuracy = D2 - D1) %>%
  select(-c(D1, D2)) %>%
  # 
  left_join(
    params %>%
      filter(
        study %in% c("Study 2", "Study 3"),
        measurement_id %in% c("D1", "D2"),
        model == "sr",
        param_name == "sr_gamma"
      ) %>%
      select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
      pivot_wider(names_from = measurement_id, values_from = sr_gamma) %>%
      mutate(delta_sr = D2 - D1) %>%
      select(-c(D1, D2)),
    by = c("study", "sub_id")
  ) %>%
  group_by(shortest_path) %>%
  nest() %>%
  mutate(
    test = map(
      .x = data,
      .f = ~with(
        .x,
        cor.test(
          delta_accuracy, delta_sr,
          method = "spearman", exact = FALSE, alternative = "greater"
        )
      ) %>% tidy()
    )
  ) %>%
  unnest(test) %>%
  ungroup() %>%
  select(-data) %>%
  kbl(
    caption = str_c(
      "<center>", "∆ Accuracy ~ ∆ SR gamma (after overnight rest)", "</center>"
    ),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive"))
```

```{r plot-gamma-accuracy}
plot_gamma_accuracy_change <- bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2")) %>%
  group_by(study, sub_id, measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  pivot_wider(names_from = measurement_id, values_from = accuracy) %>%
  mutate(delta_accuracy = D2 - D1) %>%
  select(-c(D1, D2)) %>%
  # 
  left_join(
    params %>%
      filter(
        study %in% c("Study 2", "Study 3"),
        measurement_id %in% c("D1", "D2"),
        model == "sr",
        param_name == "sr_gamma"
      ) %>%
      select(study, sub_id, measurement_id, sr_gamma = param_value) %>%
      pivot_wider(names_from = measurement_id, values_from = sr_gamma) %>%
      mutate(delta_sr = D2 - D1) %>%
      select(-c(D1, D2)),
    by = c("study", "sub_id")
  ) %>%
  ggplot(aes(x=delta_sr, y=delta_accuracy, color=shortest_path)) +
  theme_custom() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  geom_point(alpha = 0.25, show.legend = FALSE) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 1.5) +
  scale_x_continuous(name = str_c("Change in ", unicode_greek["gamma"])) +
  scale_y_continuous(name = "Change in accuracy") +
  scale_color_manual(
    name = "Shortest path distance",
    values = c("#88CCEE", "#CC6677", "#DDCC77")
  ) +
  coord_cartesian(xlim = c(-1, 1.1)) +
  theme(legend.position = "bottom") +
  ggtitle(
    str_c(
      unicode_greek["Delta"], "Navigation ~ ",
      unicode_greek["Delta"], unicode_greek["gamma"]
    )
  )

plot_gamma_accuracy_change

if (knitting) {
  suppressWarnings(
    ggsave(
      here("figures", str_c("gamma_accuracy_change", ".pdf")),
      plot = plot_gamma_accuracy_change,
      width = 8/3, height = 2.5,
      units = "in", dpi = 300
    )
  )
  
  ggsave(
    here("figures", str_c("gamma_accuracy_change_cairo", ".pdf")),
    plot = plot_gamma_accuracy_change,
    width = 8/3, height = 2.5,
    units = "in", dpi = 300,
    device = cairo_pdf
  )
}
```


# Evidence of cached representation

Here, we're trying to get a sense for what is being cached, and trying to see if there's evidence that caching (as opposed to model-based planning) is the primary driver of navigation improvement after overnight rest.

## Simulation visualization

```{r plot-network-with-sr}
butterfly_layout <- create_layout(g, layout = "stress")

plot_network_with_sr <- sim_sr_matrix_asymptotic %>%
  mutate(
    from_sorted = if_else(from < to, from, to),
    to_sorted = if_else(from < to, to, from)
  ) %>%
  group_by(gamma, from = from_sorted, to = to_sorted) %>%
  summarise(sr_value = mean(sr_value)) %>%
  filter(round(gamma, 1) %in% c(0.1, 0.5, 0.9)) %>%
  filter(sr_value > 0.05) %>%
  left_join(adjlist) %>%
  filter(from < to) %>%
  mutate(
    edge = factor(edge),
    gamma = str_c(unicode_greek["gamma"], " = ", gamma)
  ) %>%
  tbl_graph(edges = ., directed = FALSE) %>%
  mutate(name = row_number()) %>%
  ggraph("manual", x=butterfly_layout$x, y=butterfly_layout$y) +
  theme_network() +
  facet_edges(~gamma, ncol = 1) +
  geom_edge_link(aes(alpha = sr_value, color = edge)) +
  geom_node_label(aes(label = name)) +
  scale_edge_color_manual(
    name = NULL,
    values = c("0"="red", "1"="black"),
    labels = c("0"="Inferred connections", "1"="Observed connections")
  ) +
  scale_edge_alpha(name = "p(Target | Source)") +
  theme(
    legend.position = "bottom",
    legend.box = "vertical",
    strip.background = element_blank(),
    strip.text = element_text(size = 13)
  ) +
  ggtitle("Cognitive maps predicted by SR")

plot_network_with_sr

if (knitting) {
  suppressWarnings(
    ggsave(
      here("figures", str_c("network_with_sr", ".pdf")),
      plot = plot_network_with_sr,
      width = 3.5, height = 6,
      units = "in", dpi = 300
    )
  )
  
  ggsave(
    here("figures", str_c("network_with_sr_cairo", ".pdf")),
    plot = plot_network_with_sr,
    width = 3.5, height = 6,
    units = "in", dpi = 300,
    device = cairo_pdf
  )
}
```

## Transition reevaluation

Following transition reevaluation, a caching account predicts that people's navigation should get worse relative to their performance after overnight rest. In contrast, a planning account predicts that people's navigation should not be greatly impacted by a relatively small set of changes.

```{r stats-reevaluation}
stats_nav_tr_dist2 <- bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
  mutate(
    measurement_id = fct_relevel(measurement_id, "D2b"),
    shortest_path = fct_relevel(shortest_path, "2")
  ) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

stats_nav_tr_dist3 <- bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
  mutate(
    measurement_id = fct_relevel(measurement_id, "D2b"),
    shortest_path = fct_relevel(shortest_path, "3")
  ) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

stats_nav_tr_dist4 <- bind_rows(nav_study2, nav_study3) %>%
  filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
  mutate(
    measurement_id = fct_relevel(measurement_id, "D2b"),
    shortest_path = fct_relevel(shortest_path, "4")
  ) %>%
  glmmTMB(
    correct ~ shortest_path * measurement_id +
      (1 + shortest_path + measurement_id | sub_id) + (1 | study),
    family = binomial,
    data = .
  )

map_dfr(
  .x = list(
    "dist-2" = stats_nav_tr_dist2,
    "dist-3" = stats_nav_tr_dist3,
    "dist-4" = stats_nav_tr_dist4
  ),
  .f = ~tidy(.x, conf.int = TRUE),
  .id = "ref_cat"
) %>%
  check_significance() %>%
  select(-c(ref_cat, effect, component)) %>%
  kbl(
    caption = str_c(
      "<center>", "Navigation accuracy: Transition Reevaluation", "</center>"
    ),
    digits = 3
  ) %>%
  kable_styling(bootstrap_options = c("responsive")) %>%
  pack_rows("Ref. Cat. dist-2", 1, 25) %>%
  pack_rows("Ref. Cat. dist-3", 26, 50) %>%
  pack_rows("Ref. Cat. dist-4", 51, 75)
```

```{r plot-tr}
predict_tr <- expand_grid(
  measurement_id = c("D1", "D2", "D2b"),
  shortest_path = factor(2:4),
  sub_id = NA, study = NA
) %>%
  predict_glmmTMB(stats_nav_tr_dist2)

plot_nav_tr <- nav_study3 %>%
  filter(measurement_id %in% c("D1", "D2", "D2b")) %>%
  group_by(sub_id, measurement_id, shortest_path) %>%
  summarise(accuracy = mean(correct), .groups = "drop") %>%
  ggplot(aes(x=shortest_path, y=accuracy, color=measurement_id)) +
  theme_custom() +
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_dotplot(
    aes(fill = measurement_id),
    binwidth = 0.01,
    binaxis = "y", stackdir = "center",
    position = position_dodge(width = 0.75),
    dotsize = 1, alpha = 0.5, color = NA,
    show.legend = FALSE
  ) +
  geom_pointrange(
    aes(
      x = shortest_path, y = fit,
      ymin = fit - se.fit, ymax = fit + se.fit,
      color = measurement_id
    ),
    data = predict_tr, inherit.aes = FALSE, show.legend = FALSE,
    position = position_dodge(width = 0.25), linewidth = 1
  ) +
  geom_line(
    aes(
      x = shortest_path, y = fit,
      group = measurement_id, color = measurement_id
    ),
    data = predict_tr, inherit.aes = FALSE,
    position = position_dodge(width = 0.25), linewidth = 1
  ) +
  scale_x_discrete(name = "Shortest path distance") +
  scale_y_continuous(
    name = "Accuracy", labels = scales::percent, breaks = seq(0, 1, 0.25)
  ) +
  scale_color_manual(
    name = "Measurement",
    values = c("D1"="#fa9fb5", "D2"="#7a0177", "D2b"="#238b45"),
    labels = c(
      "D1"="Before overnight rest",
      "D2"="After overnight rest",
      "D2b"="After reevaluation"
    )
  ) +
  scale_fill_manual(
    values = c("D1"="#fa9fb5", "D2"="#7a0177", "D2b"="#238b45")
  ) +
  coord_cartesian(ylim = c(0.3, 1.1)) +
  theme(legend.position = "bottom") +
  ggtitle("Navigation after transition reevaluation")

plot_nav_tr

if (knitting) {
  ggsave(
    here("figures", str_c("navigation_reevaluation", ".pdf")),
    plot = plot_nav_tr,
    width = 4, height = 4,
    units = "in", dpi = 300
  )
}
```


# Session info

For reproducibility.

```{r session-info}
sessionInfo()
```

